{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/timeseriesAI/tsai/blob/master/tutorial_nbs/10_Time_Series_Classification_and_Regression_with_MiniRocket.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "created by Malcolm McLean and Ignacio Oguiza (oguiza@timeseriesAI.co) based on: \n",
    "\n",
    "* Dempster, A., Schmidt, D. F., & Webb, G. I. (2020). MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification. arXiv preprint arXiv:2012.08791.\n",
    "\n",
    "* Original paper: https://arxiv.org/abs/2012.08791\n",
    "\n",
    "* Original code:  https://github.com/angus924/minirocket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MiniRocket 🚀\n",
    "\n",
    "> A Very Fast (Almost) Deterministic Transform for Time Series Classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROCKET is a type of time series classification and regression methods that is different to the \n",
    "ones you may be familiar with. Typical machine learning classifiers will \n",
    "optimize the weights of convolutions, fully-connected, and pooling layers, \n",
    "learning a configuration of weights that classifies the time series.\n",
    "\n",
    "In contrast, ROCKET applies a large number of fixed, non-trainable, independent convolutions \n",
    "to the timeseries. It then extracts a number of features from each convolution \n",
    "output (a form of pooling), generating typically 10000 features per sample. (These \n",
    "features are simply floating point numbers.)\n",
    "\n",
    "The features are stored so that they can be used multiple times. \n",
    "It then learns a simple linear head to predict each time series sample from its features. \n",
    "Typical PyTorch heads might be based on Linear layers. When the number of training samples is small,\n",
    "sklearn's RidgeClassifier is often used.\n",
    "\n",
    "The convolutions' fixed weights and the pooling method have been chosen experimentally to \n",
    "effectively predict a broad range of real-world time series.\n",
    "\n",
    "The original ROCKET method used a selection of fixed convolutions with weights \n",
    "chosen according to a random distribution. Building upon the lessons learned \n",
    "from ROCKET, MiniRocket refines the convolutions to a specific pre-defined set \n",
    "that proved to be at least as effective ROCKET's. It is also much faster \n",
    "to calculate than the original ROCKET. Actually, the paper authors \"suggest that MiniRocket should now be considered and used as the default variant of Rocket.\"\n",
    "\n",
    "MiniROCKET was implemented in Python using numba acceleration and mathematical \n",
    "speedups specific to the algorithm. It runs quite fast, utilizing CPU cores in \n",
    "parallel. Here we present a 2 implementations of MiniRocket: \n",
    " * a cpu version with an sklearn-like API (that can be used with small datasets - <10k samples), and\n",
    " * a PyTorch implementation of MiniRocket, optimized for \n",
    "the GPU. It runs faster (3-25x depending on your GPU) than the CPU version and offers some flexibility for further experimentation.\n",
    "\n",
    "We'll demonstrate how you can use both of them througout this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries 📚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # **************** UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI & SKTIME ****************\n",
    "# stable = False # Set to True for latest pip version or False for main branch in GitHub\n",
    "# !pip install {\"tsai -U\" if stable else \"git+https://github.com/timeseriesAI/tsai.git\"} >> /dev/null\n",
    "# !pip install sktime -U  >> /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "os              : Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic\n",
      "python          : 3.7.15\n",
      "tsai            : 0.3.5\n",
      "fastai          : 2.7.10\n",
      "fastcore        : 1.5.27\n",
      "sktime          : 0.14.0\n",
      "sklearn         : 1.0.2\n",
      "torch           : 1.12.1+cu113\n",
      "device          : 1 gpu (['Tesla T4'])\n",
      "cpu cores       : 1\n",
      "threads per cpu : 2\n",
      "RAM             : 12.68 GB\n",
      "GPU memory      : [14.75] GB\n"
     ]
    }
   ],
   "source": [
    "from tsai.basics import *\n",
    "import sktime\n",
    "import sklearn\n",
    "my_setup(sktime, sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using MiniRocket 🚀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* First, create the features for each timeseries sample using the MiniRocketFeatures module (MRF). \n",
    "MRF takes a minibatch of time series samples and outputs their features. Choosing an appropriate minibatch size\n",
    "allows training sets of any size to be used without exhausting CPU or GPU memory.\n",
    "\n",
    "    Typically, 10000 features will characterize each sample. These features are relatively\n",
    "expensive to create, but once created they are fixed and may be used as the \n",
    "input for further training. They might be saved for example in memory or on disk.\n",
    "\n",
    "\n",
    "* Next, the features are sent to a linear model. The original \n",
    "MiniRocket research used sklearn's RidgeClassifier. When the number of samples \n",
    "goes beyond the capacity of RidgeClassifier, a deep learning \"Head\" can be \n",
    "used instead to learn the classification/regression from minibatches of features.\n",
    "\n",
    "For the following demos, we use the tsai package to handle timeseries efficiently and clearly. tsai is fully integrated with fastai, allowing fastai's training loop and other convenience to be used. To learn more about tsai, please check out the docs and tutorials at https://github.com/timeseriesAI/tsai\n",
    "\n",
    "Let's get started."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sklearn-type API (<10k samples) 🚶🏻‍♂️"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll first import the models we are going to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsai.models.MINIROCKET import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid accuracy    : 93.333% time: 0.526145\n"
     ]
    }
   ],
   "source": [
    "# Univariate classification with sklearn-type API\n",
    "dsid = 'OliveOil'\n",
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid)   # Download the UCR dataset\n",
    "\n",
    "# Computes MiniRocket features using the original (non-PyTorch) MiniRocket code.\n",
    "# It then sends them to a sklearn's RidgeClassifier (linear classifier).\n",
    "model = MiniRocketClassifier()\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "print(f'valid accuracy    : {model.score(X_valid, y_valid):.3%} time: {t}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid accuracy    : 65.247% time: 8.533509\n"
     ]
    }
   ],
   "source": [
    "# Multivariate classification with sklearn-type API\n",
    "dsid = 'LSST'\n",
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid)\n",
    "model = MiniRocketClassifier()\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "print(f'valid accuracy    : {model.score(X_valid, y_valid):.3%} time: {t}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to try to improve performance is to use an ensemble (that uses majority vote). Bear in mind that the ensemble will take longer since multiple models will be fitted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid accuracy    : 68.045% time: 45.066046\n"
     ]
    }
   ],
   "source": [
    "# Multivariate classification ensemble with sklearn-type API\n",
    "dsid = 'LSST'\n",
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid)\n",
    "model = MiniRocketVotingClassifier(n_estimators=5)\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "print(f'valid accuracy    : {model.score(X_valid, y_valid):.3%} time: {t}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we see an increase in accuracy although this may not be the case with other datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once a model is trained, you can always save it for future inference: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsid = 'LSST'\n",
    "X_train, y_train, X_valid, y_valid = get_UCR_data(dsid)\n",
    "model = MiniRocketClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "model.save(f'MiniRocket_{dsid}')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid accuracy    : 65.207%\n"
     ]
    }
   ],
   "source": [
    "model = load_minirocket(f'MiniRocket_{dsid}')\n",
    "print(f'valid accuracy    : {model.score(X_valid, y_valid):.3%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "153it [00:00, 6948.12it/s]\n",
      "74it [00:00, 6341.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid rmse        : 0.04162 time: 0.36316\n"
     ]
    }
   ],
   "source": [
    "# Univariate regression with sklearn-type API\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "dsid = 'Covid3Month'\n",
    "X_train, y_train, X_valid, y_valid = get_Monash_regression_data(dsid)\n",
    "rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "model = MiniRocketRegressor(scoring=rmse_scorer)\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "y_pred = model.predict(X_valid)\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
    "print(f'valid rmse        : {rmse:.5f} time: {t}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid rmse        : 0.04162 time: 1.521828\n"
     ]
    }
   ],
   "source": [
    "# Univariate regression ensemble with sklearn-type API\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "dsid = 'Covid3Month'\n",
    "X_train, y_train, X_valid, y_valid = get_Monash_regression_data(dsid)\n",
    "rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "model = MiniRocketVotingRegressor(n_estimators=5, scoring=rmse_scorer)\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "y_pred = model.predict(X_valid)\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
    "print(f'valid rmse        : {rmse:.5f} time: {t}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "119it [00:03, 30.50it/s]\n",
      "66it [00:01, 38.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid rmse        : 2.44171 time: 0.683256\n"
     ]
    }
   ],
   "source": [
    "# Multivariate regression with sklearn-type API\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "dsid = 'AppliancesEnergy'\n",
    "X_train, y_train, X_valid, y_valid = get_Monash_regression_data(dsid)\n",
    "rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "model = MiniRocketRegressor(scoring=rmse_scorer)\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "y_pred = model.predict(X_valid)\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
    "print(f'valid rmse        : {rmse:.5f} time: {t}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid rmse        : 2.28944 time: 2.696218\n"
     ]
    }
   ],
   "source": [
    "# Multivariate regression ensemble with sklearn-type API\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "dsid = 'AppliancesEnergy'\n",
    "X_train, y_train, X_valid, y_valid = get_Monash_regression_data(dsid)\n",
    "rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "model = MiniRocketVotingRegressor(n_estimators=5, scoring=rmse_scorer)\n",
    "timer.start(False)\n",
    "model.fit(X_train, y_train)\n",
    "t = timer.stop()\n",
    "y_pred = model.predict(X_valid)\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
    "print(f'valid rmse        : {rmse:.5f} time: {t}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also save this model for future inference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multivariate regression ensemble with sklearn-type API\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "dsid = 'AppliancesEnergy'\n",
    "X_train, y_train, X_valid, y_valid = get_Monash_regression_data(dsid)\n",
    "rmse_scorer = make_scorer(mean_squared_error, greater_is_better=False)\n",
    "model = MiniRocketVotingRegressor(n_estimators=5, scoring=rmse_scorer)\n",
    "model.fit(X_train, y_train)\n",
    "model.save(f'MRVRegressor_{dsid}')\n",
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid rmse        : 2.24403\n"
     ]
    }
   ],
   "source": [
    "model = load_minirocket(f'MRVRegressor_{dsid}')\n",
    "y_pred = model.predict(X_valid)\n",
    "rmse = mean_squared_error(y_valid, y_pred, squared=False)\n",
    "print(f'valid rmse        : {rmse:.5f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pytorch implementation (any # samples) 🏃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tsai.models.MINIROCKET_Pytorch import *\n",
    "from tsai.models.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Offline feature calculation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the offline calculation, all features will be calculated in a first stage and then passed to the dataloader that will create batches. This features will ramain the same throughout training.\n",
    "\n",
    "⚠️ In order to avoid leakage when using the offline feature calculation, it's important to fit MiniRocketFeatures using just the train samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the MiniRocket features and store them in memory.\n",
    "dsid = 'LSST'\n",
    "X, y, splits = get_UCR_data(dsid, split_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4925, 9996, 1), numpy.ndarray)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrf = MiniRocketFeatures(X.shape[1], X.shape[2]).to(default_device())\n",
    "X_train = X[splits[0]]\n",
    "mrf.fit(X_train)\n",
    "X_feat = get_minirocket_features(X, mrf, chunksize=1024, to_np=True)\n",
    "X_feat.shape, type(X_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "👀 Note that X_train may be a np.ndarray or a torch.Tensor. In this case we'll pass a np.ndarray. \n",
    "\n",
    "If a torch.Tensor is passed, the model will move it to the right device (cuda) if necessary, so that it matches the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll save this model, as we'll need it to create features in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path(\"./models/MRF.pt\")\n",
    "PATH.parent.mkdir(parents=True, exist_ok=True)\n",
    "torch.save(mrf.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the shape of the minirocket features is [sample_size x n_features x 1]. The last dimension (1) is added because `tsai` expects input data to have 3 dimensions, although in this case there's no longer a temporal dimension.\n",
    "\n",
    "Once the features are calculated, we'll need to train a Pytorch model. We'll use a simple linear model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (0): Flatten(start_dim=1, end_dim=-1)\n",
       "  (1): BatchNorm1d(9996, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (2): Linear(in_features=9996, out_features=14, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using tsai/fastai, create DataLoaders for the features in X_feat.\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X_feat, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "                 \n",
    "# model is a linear classifier Head\n",
    "model = build_ts_model(MiniRocketHead, dls=dls)\n",
    "model.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(valley=3.630780702224001e-05)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using tsai/fastai, create DataLoaders for the features in X_feat.\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X_feat, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "                 \n",
    "# model is a linear classifier Head\n",
    "model = build_ts_model(MiniRocketHead, dls=dls)\n",
    "                 \n",
    "# Drop into fastai and use it to find a good learning rate.\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.187912</td>\n",
       "      <td>1.731448</td>\n",
       "      <td>0.472831</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.693749</td>\n",
       "      <td>1.390600</td>\n",
       "      <td>0.552311</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.393980</td>\n",
       "      <td>1.160649</td>\n",
       "      <td>0.650446</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.169939</td>\n",
       "      <td>1.180183</td>\n",
       "      <td>0.632198</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.996974</td>\n",
       "      <td>1.137174</td>\n",
       "      <td>0.645174</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.868512</td>\n",
       "      <td>1.110967</td>\n",
       "      <td>0.654096</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.771490</td>\n",
       "      <td>1.079089</td>\n",
       "      <td>0.664639</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.699071</td>\n",
       "      <td>1.065819</td>\n",
       "      <td>0.667072</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.644864</td>\n",
       "      <td>1.066159</td>\n",
       "      <td>0.672749</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.623657</td>\n",
       "      <td>1.061442</td>\n",
       "      <td>0.671938</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "7.030589"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# As above, use tsai to bring X_feat into fastai, and train.\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X_feat, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "model = build_ts_model(MiniRocketHead, dls=dls)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "timer.start()\n",
    "learn.fit_one_cycle(10, 3e-4)\n",
    "timer.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now save the learner for inference: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('./models/MRL.pkl')\n",
    "PATH.parent.mkdir(parents=True, exist_ok=True)\n",
    "learn.export(PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inference:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For inference we'll need to follow the same process as before: \n",
    "\n",
    "1. Create the features\n",
    "2. Create predictions for those features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's recreate mrf (MiniRocketFeatures) to be able to create new features: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mrf = MiniRocketFeatures(X.shape[1], X.shape[2]).to(default_device())\n",
    "PATH = Path(\"./models/MRF.pt\")\n",
    "mrf.load_state_dict(torch.load(PATH))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll create new features. In this case we'll use the valid set to confirm the predictions accuracy matches the one at the end of training, but you can use any data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2466, 9996, 1), numpy.ndarray)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_feat = get_minirocket_features(X[splits[1]], mrf, chunksize=1024, to_np=True)\n",
    "new_feat.shape, type(new_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now load the saved learner: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('./models/MRL.pkl')\n",
    "learn = load_learner(PATH, cpu=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and pass the newly created features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['6', '6', '65', ..., '42', '42', '95'], dtype='<U2')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probas, _, preds = learn.get_X_preds(new_feat)\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6719383617193836"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(y[splits[1]], preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so the predictions match the ones at the end of training as this accuracy is the same on we got in the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Online feature calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MiniRocket can also be used online, re-calculating the features each minibatch. In this scenario, you do not calculate fixed features one time. The online mode is a bit slower than the offline scanario, but offers more flexibility. Here are some potential uses:\n",
    "\n",
    "* You can experiment with different scaling techniques (no standardization, standardize by sample, normalize, etc).\n",
    "* You can use data augmentation is applied to the original time series.\n",
    "* Another use of online calculation is to experiment with training the kernels and biases.\n",
    "To do this requires modifications to the MRF code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(valley=5.248074739938602e-05)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "model = build_ts_model(MiniRocket, dls=dls)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice 2 important differences with the offline scenario: \n",
    "\n",
    "* in this case we pass X to the dataloader instead of X_tfm. The featurew will be calculated within the model.\n",
    "* we use MiniRocket instead of MiniRocketHead. MiniRocket is a Pytorch version that calculates features on the fly before passing them to a linear head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MiniRocket(\n",
       "  (backbone): MiniRocketFeatures()\n",
       "  (head): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): BatchNorm1d(9996, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): Linear(in_features=9996, out_features=14, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "model = build_ts_model(MiniRocket, dls=dls)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.136101</td>\n",
       "      <td>1.945782</td>\n",
       "      <td>0.472425</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.638468</td>\n",
       "      <td>1.318306</td>\n",
       "      <td>0.596513</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.315844</td>\n",
       "      <td>1.139227</td>\n",
       "      <td>0.660178</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.049145</td>\n",
       "      <td>1.061230</td>\n",
       "      <td>0.666261</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.845546</td>\n",
       "      <td>1.016653</td>\n",
       "      <td>0.671938</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.702628</td>\n",
       "      <td>1.012920</td>\n",
       "      <td>0.676805</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.604361</td>\n",
       "      <td>1.000566</td>\n",
       "      <td>0.675994</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.523132</td>\n",
       "      <td>0.987853</td>\n",
       "      <td>0.673966</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.467956</td>\n",
       "      <td>0.981170</td>\n",
       "      <td>0.680049</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.431131</td>\n",
       "      <td>0.973702</td>\n",
       "      <td>0.678427</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "7.073542"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = TSStandardize(by_sample=True)\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "model = build_ts_model(MiniRocket, dls=dls)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())\n",
    "timer.start()\n",
    "learn.fit_one_cycle(10, 3e-4)\n",
    "timer.stop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we calculate the minirocket features within the model, we now have the option to use data augmentation for example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.297333</td>\n",
       "      <td>2.186477</td>\n",
       "      <td>0.416058</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.871022</td>\n",
       "      <td>1.500383</td>\n",
       "      <td>0.570154</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.535833</td>\n",
       "      <td>1.260056</td>\n",
       "      <td>0.606650</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.314667</td>\n",
       "      <td>1.177868</td>\n",
       "      <td>0.643147</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.121340</td>\n",
       "      <td>1.092441</td>\n",
       "      <td>0.660178</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.972128</td>\n",
       "      <td>1.049132</td>\n",
       "      <td>0.668694</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.858646</td>\n",
       "      <td>1.029591</td>\n",
       "      <td>0.676805</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.759472</td>\n",
       "      <td>1.035876</td>\n",
       "      <td>0.665856</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.668328</td>\n",
       "      <td>1.021052</td>\n",
       "      <td>0.683293</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.623271</td>\n",
       "      <td>1.047860</td>\n",
       "      <td>0.667478</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.568082</td>\n",
       "      <td>1.009312</td>\n",
       "      <td>0.685320</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.518211</td>\n",
       "      <td>0.998625</td>\n",
       "      <td>0.686537</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.475144</td>\n",
       "      <td>0.981528</td>\n",
       "      <td>0.688970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.445705</td>\n",
       "      <td>0.987663</td>\n",
       "      <td>0.687348</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.428531</td>\n",
       "      <td>0.982846</td>\n",
       "      <td>0.688970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.400230</td>\n",
       "      <td>0.990989</td>\n",
       "      <td>0.687753</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.381716</td>\n",
       "      <td>0.975119</td>\n",
       "      <td>0.685726</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.371274</td>\n",
       "      <td>0.981072</td>\n",
       "      <td>0.687753</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.370040</td>\n",
       "      <td>0.979608</td>\n",
       "      <td>0.686537</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.367150</td>\n",
       "      <td>0.977748</td>\n",
       "      <td>0.688970</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIcAAAEICAYAAADFr5QxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU5dn/8c+VhSSsSdgJYRMEVDAIghWquIMb7uDW1lqt+9Jqq6211qX193R7tC4tttbHVqAUN6pYpBUXEGVRZFUEZZc1gAECJJnr98eZwCQkJCGTzEzyfb9e85qZc59z5jsJzJlc5z73be6OiIiIiIiIiIg0TkmxDiAiIiIiIiIiIrGj4pCIiIiIiIiISCOm4pCIiIiIiIiISCOm4pCIiIiIiIiISCOm4pCIiIiIiIiISCOm4pCIiIiIiIiISCOm4pA0WGa20sxOj3UOERGpO2bW28zmm1mBmd1mZn80s59FYb/dzMzNLKWSdh1jREREpMGo8AuPiIiISIL4ETDd3fNiHUREREQkUannkIiIiCSyrsDiWIcQERGpLgvob3GJK/oHKQ2emaWZ2f+a2frw7X/NLC3c1sbMXjOz7WaWb2bvlX5Qm9mPzWxd+FKFz8zstPDyJDO7x8xWmNlWM5toZtnhtnQz+3t4+XYzm2Nm7WP37kVEGi4zews4BXjCzHaa2ZFm9pyZPRxuH25ma83sh2a2ycy+MrNrIrY/x8w+NrOvzWyNmT1wmDl0nBERSUARn7UFZrbEzC6MaLvOzJZGtB0XXp5rZi+Z2ebwZ/ET4eUPmNnfI7Yvc3mymb1tZo+Y2UxgN9DDzK6JeI0vzOz75fKNCl86/XU45wgzu9TM5pVb7wdm9mrd/aSkMVBxSBqDnwInAHnAscBg4L5w2w+BtUBboD3wE8DNrDdwC3C8u7cAzgJWhre5FbgAOBnoBGwDngy3fRtoBeQCrYEbgMK6e2siIo2Xu58KvAfc4u7N3X1ZBat1IPhczgGuBZ40s6xw2y7gW0AmcA5wo5ldcBhRdJwREUlMK4BvEnyu/gL4u5l1NLNLgQcIjhEtgfOBrWaWDLwGrAK6ERxbJtTg9a4GrgdahPexCTg3/BrXAL+PKEINBp4H7iY4Tp1EcJyYDHQ3s77l9vt8jd65SDkqDkljcCXwoLtvcvfNBB/8V4fbioCOQFd3L3L399zdgRIgDTjKzFLdfaW7rwhvcwPwU3df6+57CQ4cl4TPChQRfFnv6e4l7j7P3b+ut3cqIiLlFREcA4rcfQqwE+gN4O5vu/tCdw+5+wJgPEFBpqZ0nBERSUDu/k93Xx8+DvwD+JygwP894H/cfY4Hlrv7qnBbJ+Bud9/l7nvcfUYNXvI5d1/s7sXhY8Lr7r4i/BrvAG8SFKsgOKHxrLtPC+db5+6fho8L/wCuAjCzowkKVa9F4UcijZiKQ9IYdCKozJdaFV4G8GtgOfBmuCvnPQDuvhy4g+AL+SYzm2Bmpdt0BV4Od+ffDiwl+JLfHvgbMBWYEL604H/MLLVu356IiBzCVncvjni+G2gOYGZDzGx6+NKAHQRFmTaH8Ro6zoiIJCAz+1b4sq3Sz9tjCI4DuQS9isrLBVaVO67UxJpyrz/SzD4IX3a8HTibA8ehyjIA/B9whZkZwcmIieGikchhU3FIGoP1BF+0S3UJL8PdC9z9h+7eg6C76A9Kx3xw93HuPiy8rQP/L7z9GmCku2dG3NLD1fwid/+Fux8FnEjQTfRb9fIuRUSkpsYRdM/PdfdWwB8BO4z96DgjIpJgzKwr8AzBJb6t3T0TWERwHFgDHFHBZmuALqXjCJWzC2ga8bxDBet4xOunAS8CvwHah19/CgeOQ5VlwN0/APYR9DK6guDEgUitqDgkjcF44D4za2tmbYD7gb8DmNm5ZtYzXHXfQXBmNmRmvc3s1PCH9h6C8RxC4f39EXgkfEAhvN9R4cenmFm/8PXIXxN0/w8hIiLxqAWQ7+57wmM7XHGY+9FxRkQk8TQjKNZsBrBgwoJjwm1/Bu4ys4EW6Bn+TJ4NfAU8ambNLJgkYGh4m/nASWbWxcxaAfdW8fpNCC4v3gwUm9lI4MyI9r8A15jZaRZMVJBjZn0i2p8HngCKanhpm0iFVBySxuBhYC6wAFgIfBReBtAL+A/BGBSzgKfcfTrBB/WjwBZgA9COAx/wjxGcaX7TzAqAD4Ah4bYOwCSCL+xLgXdQJV9EJF7dBDwY/iy/H5h4mPvRcUZEJMG4+xLgtwSfzRuBfsDMcNs/gUcIepgWAK8A2e5eApwH9ARWE0w4MDq8zTSCsYAWAPOoYgwgdy8AbiM49mwjOEExOaJ9NuFBqglOLrxD2V6qfyMoZv0dkSiwYExEEREREREREUkEZpZBMNvZce7+eazzSOJTzyERERERERGRxHIjMEeFIYmWigbSEhEREREREZE4ZGYrCQauviDGUaQB0WVlIiIiIiIiIiKNmC4rExERERGROmVmI8zsMzNbbmb3VND+ezObH74tM7PtEW3fNrPPw7dv129yEZHGIS57DrVu3dq7d+8e6xjVUlJSQnJycqxjVIuy1o1EygqJlVdZDzZv3rwt7t62zl8ojsXjMSLe/q3GWx6Iv0zxlgfiL1O85YH4yxRveSA+jxNmlgwsA84gmN1pDnB5eLaoita/FRjg7t81s2yC2QAHEUw7Pg8Y6O7bKnu9eDxOHEo8/juqjLLWDWWtG4mUFWL/90RcjjmUm5vL3LlzYx2jWvLz88nOzo51jGpR1rqRSFkhsfIq68HMbFWdv0ici8djRLz9W423PBB/meItD8RfpnjLA/GXKd7yQNweJwYDy939CwAzmwCMAiosDgGXAz8PPz4LmObu+eFtpwEjgPGVvVg8HicOJR7/HVVGWeuGstaNRMoKsf97Ii6LQyIiIiIi0mDkAGsinq8FhlS0opl1BboDbx1i25wKtrseuB4gJyeH/Pz82qeuJ4WFhQmTV1nrhrLWjUTKCrHPq+KQiIiIiIjEizHAJHcvqclG7j4WGAuQl5fn6i1QN5S1bihr3UikrBD7vBqQWkRERERE6tI6IDfieefwsoqMoewlYzXZVkREDpN6DolIwigqKmLt2rXs2bMn1lEOEgqF2LhxY9T2l56eTufOnUlNTY3aPkVERGJkDtDLzLoTFHbGAFeUX8nM+gBZwKyIxVOBX5pZVvj5mcC9NQ2g7xAiIoem4pCIJIy1a9fSokULunXrhpnFOk4ZxcXFpKRE5yPV3dm6dStr164lkWZbERERqYi7F5vZLQSFnmTgWXdfbGYPAnPdfXJ41THABI+YTtnd883sIYICE8CDpYNT14S+Q4iIHJqKQyKSMPbs2ROXX+qizcxo3bo1mzdvjnUUERGRqHD3KcCUcsvuL/f8gUq2fRZ4tjavr+8QIiKHpjGHRCShNPQvdaUay/sUERGpL43l2NpY3qeIRFdc9hwqDnnVK4mISKOUtGsjzH4GmrWF5u2gWTto3hbSWoK+EIuIiIhULRSC3Vug4Cso2ABfr4ddmyElHTKyICMT0jMPPM7IgtSm0Xt9d9hbAHu2Q+E2KAzflz4v3gdNmkFaC0hrDk1K75uXfZ6Sru9/lQmVBL/T0t9x6X0l4rI4tL2wONYRREQOsn37dsaNG8dNN91Uo+3OPvtsxo0bR2ZmZh0li19m9ixwLrDJ3Y+poN2Ax4Czgd3Ad9z9o0PtM2nXZphy18ENyWnhYlFbaN4+KBg1axexLKKQlJ6pLxIiIhLXmjdvzs6dO1m/fj233XYbkyZNOmid4cOH85vf/IZBgwbFIKHEJfeguBJZDCj46uACwc6NEKrh391JqWSmt4Km2QcXjtIzDzwOlVRS9Cn32Etq/34tuZLiUXOaWRpkdYKmbYLvgs3aQrM2B+5TM2r/+qVCoeB97docvm0pex8qqrzAFX6eVFgCqfuCglhq08q/q7rD7vyDf6f7f88Rv2MPlf+BVfoW4rQ4VEQo5CQl6Yu7iMSP7du389RTTx1UHCouPvSBdcqUKYdsb+CeA54Anq+kfSTQK3wbAjwdvq9Ucbtj4IdTYdcm2LkpOODu3BR+vjm437EW1n8UtB10UASSmxxcMKqwkNQu+JKjQpKIiMRIp06dKiwMSQPkDkW7Ye9O2Lcz6Fmzb2f1nu/ZcaAoULL34H2nZ0KLjtCyI7TtAy06BM8j75u1C7atqCdP4XbYs5192zeQHioMlu3cAJuXQuEO2LujgjdkET2QwoWjzC6VF5UiC07JacF7q+r9l1lWcKCtYCMphdthWX7FPw8IijKlxaKmbSIKR6VFpDbQtDUU7Qm+U+7eUkHhJ/x499ZKil0WFNOSUg+8n0qUOY1sSUG+iGIXSclBwadgA5TsO3gHGdkHfpftjg5+1+V/z83awS8qnsmwyuKQmeUSfKlvDzgw1t0fK7fOlcCPg3dOAXCju38SblsZXlYCFLt7lWXtohJnxvItnHRk26pWFRGpN/fccw8rVqwgLy+P1NRU0tPTycrK4tNPP2XJkiVccMEFrFmzhj179nD77bdz/fXXA9CtWzfmzp3Lzp07GTlyJMOGDeP9998nJyeHV199lYyMKJ61iDPu/q6ZdTvEKqOA58Mz03xgZplm1tHdvzrkjlu0D25VCYWgMD8oHu3cWHEhqeAr2LAgfFangkJfUsrBBaNyhaUkWkCrFpCsaYNFRKRi99xzD7m5udx8880APPDAA6SkpPDWW2+xfft2ioqKePjhhxk1alSZ7VauXMm5557LokWLKCws5JprruGTTz6hT58+FBYWxuKtSLTs2w1v3kerT6dA0a6gcFDRSa2KpDYr2wslrSXkDg4XAsoXBTpUv5dMSpOg50pmlwqbd+fnk56dfXBDqCQoUBVuCwobGVlBpqRaDHOcES4cHaYd+flkZ2UFP9ddm2HX1gM9e3ZHFHZ2bQ6fWPw4WF5Vj6q0VtCsdfB9MLs75B4f0Tup3H1GNiRHlF1CoeB3fVDBq4Cd+RtonuoRbeUKXiVF0OUb5X63nYL75u0hNf2wf1ZQvZ5DxcAP3f0jM2sBzDOzae6+JGKdL4GT3X2bmY0ExlL2zO8p7r6luqGSzZgwZ7WKQyJSqV/8azFL1n8d1X0e1aklPz/v6ErbH330URYtWsT8+fN5++23Oeecc1i0aBHdu3enuLiYZ599luzsbAoLCzn++OO5+OKLad26dZl9fP7554wfP55nnnmGyy67jBdffJGrrroqqu8jweQAayKerw0vK1McMrPrgesBcnJyyM+vySzGSZDaAbI6QNYhVvMQtmcHtnsLSbu3kFS4Bdu1+cDj3VtI+noDtmERSbu3YKGi/ZtmAm7JhFrmUpLZjVBmt4j77oRadAqKTPWksLCwhj+juhdvmeItD8RfpnjLA/GXKd7ySDW9cQ9sWBjdfXboByMfPeQqo0eP5o477thfHJo4cSJTp07lpptuIjs7my1btnDCCSdw/vnnVzqo9NNPP03Tpk1ZunQpCxYs4Ljjjovu+2gsivfB51OhxylBYSUWNn8G//wObFpCcc+zSW7dpWwvkbQWB19+VLqsSbOgF0k8SUoOesg0raBwFEtm4XGLWkB2j6rXL708bPfWA72DUpuWLfqkpB1+nqSkA3nK2ZefDxUV3upJld9Uw2dvvwo/LjCzpQRf3JdErPN+xCYfAJ1rE6pVRgpvLt7I5oK9tG1Rix+8iEgdGjx4MN27d9///PHHH+fll18GYM2aNXz++ecHFYe6d+9OXl4eAAMHDmTlypX1ljeRuftYghMP5OXleXadHTjbAEdUJ1DwxWHnZti5kZ3rP6X5ng0k568geesKWDq3bLfhpFTI6grZR0DrI4IvJ62PCJ636hz1L3j5+fnU3c/o8MRbpnjLA/GXKd7yQPxlirc8Et8GDBjApk2bWL9+PZs3byYrK4sOHTpw++23M2PGDJKSkli3bh0bN26kQ4cOFe7j3Xff5bbbbgOgf//+9O/fvz7fQsOwdQW8eG3QSySzK4x6ArqfVL8Z5o+H138Q9Oa56kV2ZR9Hmj5L4kNS0oEiV5tesU5Tr2p0GjN8acAA4MNDrHYt8EbEcwfeNDMH/hT+gl/RvvefFe6Qk0txyPnbjGV8Z0hOTSLWu0Q6Y6SsdSORskJi5S2fNRQK7R/f56cje9fJax5q/KDStuLiYkpKSmjatOn+ZdOnT2fatGm89957NG3alNNOO41du3aV2aa4uJi0tLT9y8yMffv2VfqaoVAoYX5XtbAOyI143jm8LL6Zha+Jz4K2R7Kv1dFlz/S4B5ev5a8IvoTuv/8CVr4XjCdQKrkJZHU/uGjU+oigq3BtumNL7BTv3T8+Q5mxG8qP37B/2Q5apjSDtj0hq1twy+4e3OvfgUh0VdHDpy5deumlTJo0iQ0bNjB69GheeOEFtmzZwrx580hNTaVbt27s2bMnZvkavAX/hNfuDD5Tz3wY5j4L/3ceDLoWzvhFhb05omrfbphyN8z/O3QdChf/GVp2gob/fU8SQLWLQ2bWHHgRuMPdK7yWw8xOISgODYtYPMzd15lZO2CamX3q7u+W37b8WeEju2Xzr8VbuXPEMZV2q4wHiXTGSFnrRiJlhcTKWz7rxo0bSUmJ3Tj6WVlZFBQUkJKSQnJyMma2P09BQQHZ2dm0bNmSTz/9lA8//JDk5OT97SkpKWUeAyQlJZGUlFTpe0pKSkqY31UtTAZuMbMJBJcj76hyvKFEYHZgXKSuJ5Ztcw/GOSpfNNq6Ala8BcURfxSkpB+icNSx/gbKPuj6+K/LXg+/9+sqBojcSctQCNJbBGdJU5sF902aBl21D2dZatO6L5iEQsF7q1aBp1whqGjXIXZskN7qwACcGVmQmYt/vQnWzYXFL5cd1DK5SXB2u7RoFFk4yuwau0siRKTGRo8ezXXXXceWLVt45513mDhxIm3btiU1NZXp06ezatWqQ25/0kknMW7cOE499VQWLVrEggUL6il5gtu7E974Ecx/AXJPCIoymblBUeith+GDp+DzaTDqD9BjeN1k2PQp/PPbweVkJ90NJ99TdiwakRir1r9GM0slKAy94O4vVbJOf+DPwEh331q63N3Xhe83mdnLwGDgoOJQeWMG5/KDiZ/wwRf5fOOI1lWtLiJS51q3bs3QoUM55phjyMjIoH37AwMin3XWWTzzzDP07duX3r17c8IJJ8Qwafwws/HAcKCNma0Ffg6kArj7H4EpBNPYLyeYyv6a2CStR2bBWcKWnaD7N8u2hULw9bqgWBRZONryOXz+ZtmZKVKbBgWj8kWj7CPAU6CkODyIYUGFgx6WGeTwoJk+CsoNhriToCNwVe8tqdy0rOHHzdsR2rcPKIKiwnDxZHfweN+u4L6ymUQOJSX9EEWk8ONKljUp3AvJxQeKPZXMynLIgUFTMsoVeLpCx7wDA2iWn3mldL20lhVeSlhQWhAvKQoGxty2ErZ9Gb5fCflfwpoPg4JVpGbtKi4cZXWD5h3U60gkjhx99NEUFBSQk5NDx44dufLKKzn33HPp168fgwYNok+fPofc/sYbb+Saa66hb9++9O3bl4EDB9ZT8gT21QKYdE1wTD3pR3Dyjw8UZZo0hRG/hKNGwas3wfOjYOA1cMaDkN4yehk+fgGm3BUcg65+CY44NXr7FomS6sxWZsBfgKXu/rtK1ukCvARc7e7LIpY3A5LCYxU1A84EHqxOsLP7deSByYsZP3u1ikMiEjfGjRtX4fK0tDTeeOONCttKxxVq06YNixYt2r/8rrvuinq+eOPul1fR7sDN9RQn/iUlBWcyM3Ohx8ll20IlQcGgfG+jTUvgsyllZtbISkqpeqaNUslNys500qR5MG1rZtcDAybub29RQfEnoj21aaW9mXZW1WsxVHJwwaioMOiBU2bZ7qrX2/M1FGyMWLY7eBxR6DnQ18YOLuRkda+guFPBtLvVnfmlppJTgwJPdnfglLJt7kHxqqLC0eoPYNGksgWtlPSyvY4iC0eZXYM/jOpLZTO07Nt18IwspfflHrcsKoImaWDJQYFt/31SMOj7QctKn6dUsCzivrrLSrdPSgFLpknhHmjRKrz/yFtyDZ9Xsk4c956Xw7dw4YHBsNu0acOMGTMq7EW8c2cwdl23bt32f3/IyMhgwoQJ9RM0mkKhoLi95NXgc+f47wUnSeqSO3zwR5j2s+C49u3JlY8t1GUI3DADpv8SZj0R9CI6/3HoeVrtMuzbBa/fBZ+Mg27fhIueCaYXF4lD1ek5NBS4GlhoZvPDy34CdIH9Z37vB1oDT4UvASudsr498HJ4WQowzt3/XZ1g6anJXHRcZ8Z9uJptu/aR1axJDd6WiIhIA5OUHAxqndX14DOOJcWwYzVsDXoc7dn0BRktssvObFK+kFNa5KnNjBvRlJRc6ewdUeEe9MgJF4y2b91MZoeutZ9mt76ZHRgoM6eCWYqK98GONQcXjratglUzyw6UDkHPoooKR1ndwZMrmU53V7meaJHFnl0VrB+xTXWVn6K5SQto3oFQcTGkJAfFz1BJUAgLlYDvC9+XhNtC4cclB+4jH++/D0XsK7ysOr3kSn981X9Hh8dqWmSKs9mLpHFzh3XzYNFLsOSVoGdschqEimDm49D/MjjxVmjXN/qvvWsrzV+7Dr78Lxw5AkY9FUw9fiipGXDmQ9D3/KAX0d8vguO+FYxNlN6q5hk2LYWJ34Yty4LeSif/WP9HJa5VZ7ayGcAhT1u4+/eA71Ww/Avg2BqnCp/xGjM4l+feX8lLH6/j2mHdq9hIRESkkUpOOXCJGadTmJ9PRsMfr6pmzCClSXDLyCJUnB70/mloUpoElxe2rmDWPfdgat7SotG2LyE//HjlDFjwDyILIzX6F5TatOwUzE2aQ/P2wWWOkcXI8utUNGVzarNKC3ZV9kCLBvdyhabSxxEFp1AxeAnbt20ls0WL8HrFB9Y/5PPqrHO42xTV7c9GpCru8NUnsPilYPy07auDGTt7ng6nPwC9RwafQ7OehI/+FowBdOQIOPG2YIy+aPSWWzkDXryO1F1bYMSjMOSGmu0393j4/nvw9q/g/cdh+X/hvMeh1+nV2949eF+v3xV8tl39MhxxStXbicRYXI6AlRQ+s9SnQ0vycjMZP3s13x3aLa4HphYRERGJa2bQrE1w6zzo4PbivcEfcuHiUeGW1WS0bF294k5DOhtuFh6PJAU4dM+6kLcsO1NhPLhK35cr4+6N4u+J4IrterZxCSx6MSgI5a8IerL1GB4MutznnLLF+LQWcPavg7Y5f4bZf4LnzoacQTD0Nuhz7uF9ppQUw7v/A+/+GrK68/VlL9Gqzzer3q4iqenB7GWlvYheuBjyroKzHjn0iYW9O+H1H8KCCcElbBf9OZicQiQBxGVxyCJm+Lh8cC4/fnEh81ZtY1C3ODv4ioiIiDQUKWnQpldwA/VAkwYlPT2drVu30rp16wZdIHJ3tm7dSnp6et2/2JbPg0vGFr8Emz8NxuTq9s2gwNP3/ODy10Np1hqG/zi4tOyTcfD+EzDxW0Ev2G/cAnlXVH9stx1r4cXrYPX7cOwVcPavKdm1r+rtqtJ5IHz/XXjn/8GM/4UV/4XzHoMjzzp43Y1LgtnItnwOw+8NZiRrSIVzafDisjgUeU36uf078dBrSxk/e42KQyIiIiIiUmOdO3dm7dq1bN68OdZRDhIKhUiK4thn6enpdO7cOWr7i5S0YzUs/issehk2LgQsuBzs7N8EM341b1fznZYOUD3wGlj6L5j5GLz+g2Bw6CHfD9oOVWha+hq8enNweeWFY+HY0cHyXfmH9R4PkpIGp90Pfc+DV26CcZcFBagRvwwmKXCHj/8GU34UzHD2rVcPnlRCJAHEZXHIivfC7nxomk2ztBTOz+vESx+t5f7zjqJVRmqs44mIiIiISAJJTU2le/f4HMM0vz7G0qqNgo1B76AFE8lc/1GwrPPgYDyfo0ZFb9axpGQ4+oJgn6tmBoNWT38EZvweBlwN37g5mJShVNEeePOnwaVpHfPgkmcrHm8tWjoNgOvfhnd/A+/9Fla8BSMfhc/eCMZs6zE8mI3scApkInEgTqfncFg9a/+zy4/vwp6iEK/OXxfDTCIiNdO8eTCPzfr167nkkksqXGf48OHMnTu3PmOJiIiIHNreAvhkAvztQvhdH/j3PRAqZvewe+GOhfC9aXDCjXUzHb0ZdBsGV06Emz6Aoy+Euc/C4wNg0ndh/XzY/Bn8+bSgMPSNW+DaaXVbGCqVkgan/hSueysYv+2f34GF/4RTfgpXvaTCkCS0uOw5hCXBypnB4GVAv86tOCanJeNnr+HqE7o26OuERaTh6dSpE5MmTYp1DBEREZHKlRQFvWEWTIRPX4fiQsjsAsN+EEw737Y3e/LzaZpZj72c2vWFC56CU++DD56GuX8NBr5OSg2ml79yEvQ6o/7ylOqUB9dNh3l/hQ79gkvrRBJcXBaHPDUDVs0os2zM8V2475VFLFi7g2NzG+DUsyIS9+655x5yc3O5+eabAXjggQdISUlh+vTp5OfnU1xczMMPP8yoUaPKbLdy5UrOPfdcFi1aRGFhIddccw2ffPIJffr0obCwMBZvRURERCQYL2ftnKAgtPilYJr5jKxgMOj+l0HukOhML19bLTvBmQ/BSXfBvOdg2yo4+UfQokPsMqU0CcZEEmkg4rQ41Bw2LIQ9O4KKMDAqrxOPvL6U8bNXqzgkIvDGPcHnRDR16BdcO16J0aNHc8cdd+wvDk2cOJGpU6dy22230bRpU7Zv384JJ5zA+eefX2kPx6effpqmTZuydOlSFixYwHHHHRfd9yAiIiJSlS2fBwWhhRNh20pISYfeI6H/aDjitKDwEY/SW8HQ22OdQqRBis/iUJNm4Ltg9Qf7pwlskZ7Kuf07MvmT9dx37lE0T4vL6CLSgA0YMIBNmzaxfv16Nm/eTFZWFh06dODOO+/knXfeITk5mXXr1rFx40Y6dKj4TNa7777LbbfdBkD//v3p379/fb4FERERaawKNgaXZC2cCOs/Doby6H4SnPxj6HNuMNOWiDRacVlh8ZSmwXWkK2fsLw4BXD6kC/+ct5Z/fbKeywd3iWFCEYm5Q/TwqUuXXpBd/cEAACAASURBVHopkyZNYsOGDYwePZoXXniBzZs3M3v2bDIyMujWrRt79uyJSTYRERGRg2xYCNN+Dl9MBw9Bx2PhzEfgmIuhZcdYpxOROBGfs5WZQc7AYArDCANyM+ndvgXjZ6+OUTARaexGjx7NhAkTmDRpEpdeeik7duygXbt2pKamMn36dFatWnXI7U866STGjRsHwKJFi1iwYEF9xBYREZHGJhSCWU/CM6fChgXBwNI3z4bvvwsn3qLCkIiUEZ/FIYBuQ4NpCvcW7F9kZowZnMuCtTtYvH5HDMOJSGN19NFHU1BQQE5ODh07duTKK69k7ty55OXl8fzzz9OnT59Dbn/jjTeyc+dO+vbty/3338/AgQPrKbmIiIg0GgUb4IWLYepPoOfpcNOHcNrPoG3vWCcTkTgVl5eVAdB1KLz3W1gzG3qetn/xhQNyePSNT5kwew0PXdAqhgFFpLFauPDAQNht2rRh1qxZFBcXk5JS9iN1586dAHTr1o1FixYBkJGRwYQJE+ovrIiIiDQun70Br94M+3bDOb+DQd+NjxnHRCSuxW/PodwhYMkHXVqW2bQJZ/fryCsfr2P3vuIYhRMREREREYkj+3bDaz+A8WOCqd+//w4cf60KQyJSLfFbHEprDp0GwMqZBzWNOT6Xgr3FvL7gqxgEExERERGRmjCzEWb2mZktN7N7KlnnMjNbYmaLzWxcxPISM5sfvk2uv9QJZMNCGDsc5v4FvnELfO+/uoRMRGokfi8rg2DcoVlPBVXwJk33Lx7cPZsebZsxYc4aLh2UG8OAIlLf3B1rBGfA3D3WEURERKLCzJKBJ4EzgLXAHDOb7O5LItbpBdwLDHX3bWbWLmIXhe6eV6+hE0UoBB8+Df95ADKy4eqX4YhTY51KRBJQ/PYcAug6DEJFsHZOmcVmxuXHd2Heqm0s21hQycYi0tCkp6ezdevWBl84cXe2bt1Kenp6rKOIiIhEw2Bgubt/4e77gAnAqHLrXAc86e7bANx9Uz1nTDzlB52+8X0VhkTksFXZc8jMcoHngfaAA2Pd/bFy6xjwGHA2sBv4jrt/FG77NnBfeNWH3f3/qp2uyxCwpGDcoR4nl2m66Lgc/mfqp4yfvZqfn3d0tXcpIomrc+fOrF27ls2bN8c6ykFCoRBJSdGrt6enp9O5c+eo7U9ERCSGcoA1Ec/XAkPKrXMkgJnNBJKBB9z93+G2dDObCxQDj7r7K+VfwMyuB64HyMnJIT8/P7rvoA4VFhbWOG/qF/+h2X9+jBXvZvcpj7D3mMthr8Heun3fh5M1VpS1bihr3Yl13upcVlYM/NDdPzKzFsA8M5sW2Q0UGAn0Ct+GAE8DQ8wsG/g5MIigsDQv3IV0W7XSpbeCDv0qHHeodfM0zjy6Ay9/vI4fj+hDempytXYpIokrNTWV7t27xzpGhfLz88nOzo51DBERkUSVQvC3xHCgM/CumfVz9+1AV3dfZ2Y9gLfMbKG7r4jc2N3HAmMB8vLyPJGOyTX6DrFvN7x5XzC2UId+cPFfaNa2N83qNuJ+ifR9R1nrhrLWnVjnrfI0t7t/VdoLyN0LgKUE1f9Io4DnPfABkGlmHYGzgGnunh8uCE0DRtQoYddhwWVlRXsOarpicBe27y5i6uINNdqliIiIiIjUm3VA5EChncPLIq0FJrt7kbt/CSwjKBbh7uvC918AbwMD6jpwXNKg0yJSh2o0ILWZdSP4MP6wXFNFXUVzDrG8on1X2BU0tXV/WpTs5etPp1OcU7b3ae8so3NmGs/P/IJvdsmoyVuJmlh3/aoJZa0biZQVEiuvsiY+MxtBcNlxMvBnd3+0XHtX4FmgLZAPXOXua+s9qIiI1KU5QC8z605QFBoDXFFunVeAy4G/mlkbgsvMvjCzLGC3u+8NLx8K/E/9RY8DGnRaROpBtYtDZtYceBG4w92/jnaQSruCpp8Jrxst8xdCv5EHbXf5kG78eupnbC9pQo+2zaMdq0qx7vpVE8paNxIpKyRWXmVNbNWZnQb4DUHP0/8zs1OBXwFX139aERGpK+5ebGa3AFMJThY86+6LzexBYK67Tw63nWlmS4AS4G5332pmJwJ/MrMQwVUPj5Y7jjRsOzfBy9+HFW9B73Pg/D9As9axTiUiDVC1Rk81s1SCwtAL7v5SBatU1lW0Ol1ID61pNrQ/GlbOqLD50oGdSU4y/jFnTYXtIiISM9WZneYo4K3w4+kVtIuISAPg7lPc/Uh3P8LdHwkvuz9cGCI8PMUP3P0od+/n7hPCy98PPz82fP+XWL6PehUqgX9cBatmwbm/hzEvqDAkInWmOrOVGfAXYKm7/66S1SYDt5jZBIIBqXe4+1dmNhX4Zbg7KMCZwL01Ttl1KHz0PBTvg5QmZZratUzn9L7tmDRvLT88szdNUqI3W5CIiNRKdWan+QS4iODSswuBFmbW2t23Rq4U77PQxNtlhfGWB+IvU7zlgfjLFG95IP4yxVseaWBmPwNrPoQL/gh5l8c6jYg0cNW5rGwoQRf/hWY2P7zsJ0AXAHf/IzCFYBr75QRT2V8Tbss3s4cIrjMGeNDda34E7TYUZv8J1n8cTG9fzpjBXZi6eCPTlmzknP4da7x7ERGJmbuAJ8zsO8C7BL1LS8qvFO+z0MTbZYXxlgfiL1O85YH4yxRveSD+MsVbHmlAtq2E//4Cep4Bx46JdRoRaQSqLA65+wzAqljHgZsraXuWYLDRw9d1aHC/akaFxaGTerUlJzODCXNWqzgkIhI/qry02N3XE/QcKh3b7uLwtMUiIiKNkztMvg0sGc77X7BD/ikmIhIViXENVrM20LYPrJxZYXNyknHZoFze+3wLq7furudwIiJSif2z05hZE4LZaSZHrmBmbcys9Fh0L7U9mSAiIpLoPnoevnwHzvgFtOoc6zQi0kgkRnEIgt5Daz6EkuIKmy87vjNJBv+Yu7qeg4mISEXcvRgonZ1mKTCxdHYaMzs/vNpw4DMzWwa0Bx6JSVgREZF48PV6ePM+6PZNGHhNrNOISCOSOMWhbkNh30746pMKmzu2ymB473b8c+5aiktC9RxOREQqUo3ZaSa5e6/wOt9z972xTSwiIhIj7vDanVBSBOc/DkmJ86eaiCS+xPnE6TosuF9V8ZT2AGOOz2VTwV7e+nRTPYUSERERERGJgoWTYNm/4dT7ILtHrNOISCOTOMWhFu2hdU9Y9X6lq5zapx3tWqQxfrYuLRMRERERkQSxczO88SPIGQQn3BjrNCLSCCVOcQiCcYdWzYLQQbMcA5CSnMRlg3J5Z9lm1m8vrOdwIiIiIiIih+GNu4MhNEY9CUnJsU4jIo1QYhWHug2DvTtg46JKVxl9fC4hhwnqPSQiIiIiInEudcWbsPhlOOlH0K5PrOOISCOVWMWhricG95VMaQ+Qm92UM45qz3Pvr2RHYVE9BRMREREREamhwm00m34ftO8Hw+6IdRoRacQSqzjUqjNkdoVVlReHAG4/rRdf7ynmrzO/rKdgIiIiIiIiNTT1PqwwH0Y9AcmpsU4jIo1YYhWHILi0bNVMCFU+Xf0xOa0446j2/GXGl+o9JCIiIiIi8Wf5f2D+39kz8PvQKS/WaUSkkUu84lDXoVC4DTYvPeRqd5zei4I9xTw7Q72HREREREQkjuwtgH/dAW2OpHDwbbFOIyKSgMWhbkOD+0OMOwRwdKdWnHV0e56d8SU7dqv3kIiIiIiIxIn//AJ2rIXzn4CUtFinERFJwOJQZldo2RlWzahy1TtOP5KCvcX8ZcYX9RBMRERERESkCitnwpxnYMgN0GVIrNOIiACJWBwyC3oPrXof3A+5at+OLRl5TAeenbmS7bv31VNAERERERGRChQVwuRbgxPep/0s1mlERPZLvOIQBOMO7doMW5ZVuertp/di595i/qKxh0REREREJJam/xLyV8D5j0OTZrFOIyKyX2IWh7oNC+5XVn1pWZ8OLTmnX0f+qt5DIiIiIiISK+vmwawn4LhvQ4/hsU4jIlJGlcUhM3vWzDaZ2aJK2u82s/nh2yIzKzGz7HDbSjNbGG6bG7XU2T2geYdgSvtquO20XuzaV8wz72nsIRERERERqWfF++DVW4K/Yc58KNZpREQOUp2eQ88BIyprdPdfu3ueu+cB9wLvuHt+xCqnhNsH1S5qhNJxh1bOrHLcIYDeHVpwdr+OPDdzJfm71HtIRERERETq0Xu/hU1L4NzfQ3qrWKcRETlIlcUhd38XyK9qvbDLgfG1SlRdXYfCzg2QX73eQHec1ovdRSXqPSQiIiIiIvVnwyJ47zfQ7zLoXek5dxGRmEqJ1o7MrClBD6NbIhY78KaZOfAndx97iO2vB64HyMnJIT//0PWopKx+ZAK7Fk9l7zFjqszXOhXO7N2a52Z+ySXHZJHVNLXKbaqjsLCwyqzxQlnrRiJlhcTKq6wiIiKS0EqK4dWbIT0TRjwa6zQiIpWKWnEIOA+YWe6SsmHuvs7M2gHTzOzTcE+kg4QLR2MB8vLyPDs7+9CvlnU8NGtLsy3zaZZ9U7UC3n320bz5+3f558Jt3DOyT7W2qUp+fj5VZo0Tylo3EikrJFZeZRUREZGENusJ+Go+XPJXaNY61mlERCoVzdnKxlDukjJ3Xxe+3wS8DAyO2quZQdcTqz3uEEDPdi04/9hOPD9rJVt37o1aFBERERERkTK2fB5MXd/nXDj6wlinERE5pKgUh8ysFXAy8GrEsmZm1qL0MXAmUOGMZ4et6zD4ei1sX13tTW49tRd7ikoY+67GHhIRERERkToQCsHkWyE1Hc75bXBiW0QkjlVnKvvxwCygt5mtNbNrzewGM7shYrULgTfdfVfEsvbADDP7BJgNvO7u/45meLqeGNxXc0p7gJ7tmod7D61ii3oPiYiIiIhItM35M6yeBWf9Clp0iHUaEZEqVTnmkLtfXo11niOY8j5y2RfAsYcbrFraHQUZWcGlZXlXVHuz207rxeRP1vOnd1bw03OOqsOAIiIiIiLSqGxbBf95AI44rUZ/o4iIxFI0xxyqf0lJ0OVEWDWjRpv1aNucC/Jy+NsHq9hUsKeOwomIiJmNMLPPzGy5md1TQXsXM5tuZh+b2QIzOzsWOUVEpG5VdTwIr3OZmS0xs8VmNi5i+bfN7PPw7dv1l/owuMO/bgsuIzvvMV1OJiIJI7GLQwDdhsK2lbBjXY02u/W0XhSVOGPf0dhDIiJ1wcySgSeBkcBRwOVmVr675n3ARHcfQDCxwVP1m1JEROpadY4HZtYLuBcY6u5HA3eEl2cDPweGEExu83Mzy6rH+DXz8d/hi7fh9AcgMzfGYUREqi/xi0Ndhwb3NRh3CKB7m2ZckJfD3z9U7yERkToyGFju7l+4+z5gAjCq3DoOtAw/bgWsr8d8IiJSP6pzPLgOeNLdt8H+2Y4BzgKmuXt+uG0aMKKectfM11/B1J8Gf58MujbWaUREaqTKMYfiXod+kNYKVs6A/pfVaNNbT+3JK/PX8ce3v+D+8zT2kIhIlOUAayKeryU48xvpAeBNM7sVaAacXtGOzOx64HqAnJwc8vPzox62NgoLC+MqU7zlgfjLFG95IP4yxVseiL9M8ZYnjlXneHAkgJnNBJKBB8KT2VS0bU75F4j5ccKd5q/dQmrxHnac/BCh7durvWki/TtS1rqhrHUjkbJC7PMmfnEoKRm6nFDjnkMA3do048IBObzw4SpuOLkH7Vqm10FAERE5hMuB59z9t2b2DeBvZnaMu4ciV3L3scBYgLy8PM/Ozo5B1Mrl5+cTT5niLQ/EX6Z4ywPxlyne8kD8ZYq3PAkuBegFDAc6A++aWb/qbhzz48TCSfDlf+CMh8jsMbBGmybSvyNlrRvKWjcSKSvEPm/iX1YGwbhDW5dDwYYab3rrqT0pDjlPvb2iDoKJiDRq64DIARc6h5dFuhaYCODus4B0oE29pBMRkfpSnePBWmCyuxe5+5fAMoJiUXW2ja1dW+CNH0Gn4+CEm2KdRkTksDSM4lDXYcH9YfQe6tq6GRcfl8O42avZsENjD4mIRNEcoJeZdTezJgQDTk8ut85q4DQAM+tLUBzaXK8pRUSkrlXnePAKQa8hzKwNwWVmXwBTgTPNLCs8EPWZ4WXx440fw56vYdSTkJz4F2aISOPUMIpDHY+FJs1hZc2LQwC3ntqLUMj54zvqPSQiEi3uXgzcQvAlfinBrGSLzexBMzs/vNoPgevM7BNgPPAdd/fYJBYRkbpQzePBVGCrmS0BpgN3u/tWd88HHiIoMM0BHgwviw+fToFFk+Cku6G9xjAVkcTVMErbySmQO+Sweg4B5GY35ZKBnRk3ezU3nHwEHVpp7CERkWhw9ynAlHLL7o94vAQYWt+5RESkflXjeODAD8K38ts+Czxb1xlrrHA7vHYntDsaht0Z6zQiIrXSMHoOQTDu0OZPg2t+D8PNp/QkFHKeent5lIOJiIiIiEiD8+Z9sGszXPAkpDSJdRoRkVppOMWhWow7BEHvoUsHdWbC7DWs314YxWAiIiIiItKgrHgLPv4bnHgrdBoQ6zQiIrXWcIpDnQZASsZhjzsEQe8hR72HRERERESkEnt3wuTboXVPGH5PrNOIiERFwykOpTSB3MGH3XMIoHNWUy4dlMs/5qxhnXoPiYiIiIhIef99EHasCWYnS82IdRoRkahoOMUhgK5DYeNi2H34ExjcfEpPAJ6art5DIiIiIiISYdUsmD0WBl8PXU6IdRoRkahpWMWhbkMBh9WzDnsXOZkZjD4+l4lz17B22+7oZRMRERERkcRVVAiTb4HMXDjt/qrXFxFJIA2rOJQzCJLTYNX7tdrNzaf0xDCeVO8hEREREREBePtR2LocznsM0prHOo2ISFQ1rOJQajp0HgQrZ9RqNx1bZXDVCV0ZP3sN0z/bFKVwIiIiIiKSkNZ/DO//AQZcDUecGus0IiJRV2VxyMyeNbNNZraokvbhZrbDzOaHb/dHtI0ws8/MbLmZ1c9Q/l2HwoYFsGdHrXbzoxG96dOhBXf+Y74GpxYRERERaayK98Grt0DzdnDmw7FOIyJSJ6rTc+g5YEQV67zn7nnh24MAZpYMPAmMBI4CLjezo2oTtlq6DQUPweoPa7Wb9NRknr5qIMUlzs0vfMS+4lCUAoqIiIiISMKY8XvYuAjO+R1kZMY6jYhInaiyOOTu7wKHM/3XYGC5u3/h7vuACcCow9hPzXQeDEmpsOzftd5V9zbN+PUl/Zm/Zju/emNpFMKJiIiIiEjC2LgE3v01HHMJ9Dk71mlEROpMSpT28w0z+wRYD9zl7ouBHGBNxDprgSGV7cDMrgeuB8jJySE///Cno2/a50LS5v2Vgm4jKO406LD3AzAkJ43LB3bgrzNX0rdNE07v3bpMe2FhYa2y1idlrRuJlBUSK6+yioiISMyESoLZydJbwsj/F+s0IiJ1KhrFoY+Aru6+08zOBl4BetV0J+4+FhgLkJeX59nZ2YefaNTvYP2HtPzPXXDDjOADvRZ+cWEmn26exYP//oJBPTvSo+2B2Qny8/OpVdZ6pKx1I5GyQmLlVVYRERGJmQ+egnXz4OK/QLM2sU4jIlKnaj1bmbt/7e47w4+nAKlm1gZYB+RGrNo5vKzupbWAi8bCjjXw79qPg90kJYknrjiO1GTjphc+Yk9RSRRCioiIiIhIXNq6At56GHqfDcdcHOs0IiJ1rtbFITPrYGYWfjw4vM+twBygl5l1N7MmwBhgcm1fr9q6nADf/CHMfwGWvFrr3eVkZvD70Xl8trGA+1+tcOI2ERERERFJdO4w+VZITgsGoQ7+1BERadCqM5X9eGAW0NvM1prZtWZ2g5ndEF7lEmBReMyhx4ExHigGbgGmAkuBieGxiOrPyT+GTgPgX7fD11/VenfDe7fjllN6MnHuWibOXVP1BiIiIiIiklg2LYVVM+GUn0DLjrFOIyJSL6occ8jdL6+i/QngiUrapgBTDi9aFCSnwkXPwB+/Ca/cCFe9BEm16yx1x+lHMm/VNn72yiL65bSifVqUsoqIiIiISOxt+Sy473pibHOIiNSjWl9WFvfa9IKzHoEvpsPssbXeXXKS8diYAbTKSOWmFz5i597iKIQUEREREZG4sGV5cN/6iNjmEBGpRw2/OAQw6LvQ6yz4z8+DbqK11LZFGn+4fACr83fz0L+/wN2jEFJERERERGJuyzJolQtNmsU6iYhIvWkcxSEzGPUENGkOL10HxXtrvcshPVpz91m9mfbZVp57f2XtM4qIiIiISOxtWRZcfSAi0og0juIQQPN2cP4fYMNCmP5IVHZ5/Td7cNIRWfxyylI+Xr0tKvsUEREREZEYcYety6G1ikMi0rg0nuIQQJ+z4bhvw8zHYeWMWu8uKcl48JyetG+Zzs0vfMS2XfuiEFJEpOEwsxFm9pmZLTezeypo/72ZzQ/flpnZ9ljkFBERAaDgK9i3Uz2HRKTRaVzFIYCzfgnZ3eHlG6Cw9n+DtExP4akrj2PLzn3cOXE+oZDGHxIRATCzZOBJYCRwFHC5mR0VuY673+nuee6eB/wBeKn+k4qIiIRt+Ty4b3NkbHOIiNSzxlccSmseTG//9XqYcndUdtm/cyY/O+8o3v5sM0+9vTwq+xQRaQAGA8vd/Qt33wdMAEYdYv3LgfH1kkxERKQiW5YF9+o5JCKNTEqsA8RE50Fw8o/g7V/BkWdBv0tqvcurhnRhzpf5/G7aMo7rmsWJR7SJQlARkYSWA6yJeL4WGFLRimbWFegOvFVJ+/XA9QA5OTnk5+dHN2ktFRYWxlWmeMsD8Zcp3vJA/GWKtzwQf5niLY9EwZbPg0lsWnSMdRIRkXrVOItDAN+8Cz6fBq//ALqcAK0612p3ZsavLurH4vU7uG38fKbcNox2LdOjFFZEpMEbA0xy95KKGt19LDAWIC8vz7Ozs+szW5Xy8/OJp0zxlgfiL1O85YH4yxRveSD+MsVbHomCrZ9D657BbMciIo1I47usrFRyClw0FkqKg/GHQqFa77JZWgpPXzWQXXuLuWX8xxSX1H6fIiIJbB2QG/G8c3hZRcagS8pERCTWtnyu8YZEpFFqvMUhgNZHwMhHYeV78MFTUdnlke1b8MiFxzD7y3x+8+ayqOxTRCRBzQF6mVl3M2tCUACaXH4lM+sDZAGz6jmfiIjUk2rMXvkdM9scMYPl9yLaSiKWH3QciZp9u2DHGhWHRKRRaryXlZUacDUsmwr//QX0GA4djqn1Li86rjNzVm7jj++s4PhuWZzWt32t9ykikmjcvdjMbgGmAsnAs+6+2MweBOa6e+kX/DHABHfXdI8iIg1QxOyVZxCMPzfHzCa7+5Jyq/7D3W+pYBeF4Vkt69bWFcF9m551/lIiIvGmcfccguB64vMeg/RMeOk6KNoTld3+/LyjOLpTS+78x3y+3LIrKvsUEUk07j7F3Y909yPc/ZHwsvsjCkO4+wPuftBZZBERaTBqOntlbOyfqUw9h0Sk8VHPIYBmbeCCp+CFS+Cth+CsR2q9y/TUZJ6+ciAXPDWT7/x1Ni/deCKtm6dFIayIiIiISEKp7uyVF5vZScAy4E53L90m3czmAsXAo+7+SvkNozGrZfrahWRgbLNsqMdZ6BJp1jtlrRvKWjcSKSvEPq+KQ6V6nQHHfw9mPRE87jG81rvs0ropz3xrEFc88wHfe34u4687gfTU5FrvV0RERESkgfkXMN7d95rZ94H/A04Nt3V193Vm1gN4y8wWuvuKyI2jMqvlrjWQ2YXsdvU7jX0izXqnrHVDWetGImWF2OfVZWWRzngIWveCl2+E3dGp2A3smsVjY/KYv2Y7d0yYT0lIQ2qIiIiISKNS5eyV7r7V3feGn/4ZGBjRti58/wXwNjCgTlJuWaZLykSk0aqyOGRmz5rZJjNbVEn7lWa2wMwWmtn7ZnZsRNvK8PL54a6g8a1JU7j4Gdi1CV7/AURpbNQRx3TkvnOO4t+LN/DLKUujsk8RERERkQRR5eyVZhbZXed8YGl4eZaZpYUftwGGAuUHsq69UCgYkFrFIRFppKpzWdlzwBPA85W0fwmc7O7bzGwkQXfOyGuIT3H3LbVKWZ86DYDh9wZjD/U6E/KuiMpurx3WnTX5u/nLjC/pnJXBNUO7R2W/IiIiIiLxrJqzV95mZucTjCuUD3wnvHlf4E9mFiI4sf1oBbOc1d7X66Bot2YqE5FGq8rikLu/a2bdDtH+fsTTDwi6iSa2YXfC8v/AKzfCkslw8t2QM7Dq7arws3OPYv32Qh58bQmdMjM46+gOUQgrIiIiIhLf3H0KMKXcsvsjHt8L3FvBdu8D/eo8oGYqE5FGLtpjDl0LvBHx3IE3zWxeeAaBxJCUDFdMhOE/gdWz4JlT4W8XwapZtdptcpLx2JgB9O+cye0TPubj1duiFFhERERERA7b1uXBfetesc0hIhIjUZutzMxOISgODYtYPCw8s0A7YJqZferu71ayfa2nn4y6/tdB7zGkL3yB9I//TNJfR1CUcwKFg2+luPM3wOywppv77fk9+c4LC/nuc7N57sp+5Gal19EbKCvWU+PVhLLWnUTKq6wiIiJSL7Ysg7RW0LxdrJOIiMREVIpDZtafYFaBke6+tXR5xMwCm8zsZWAw8P/bu/P4qOp7/+Ov70wm+zrZSQIJO7LKLipabRW1St3q1rq0dWnr1drtam9/ra29rbXLvde6Va1VW/e1tKK4IlYBQQRkD6skQMjCFsg+398f5yQMISEBkplJ8n4+HvM4Z84y885kcibnM9/z/bZZHOqS4Se7hR9yfwKn3wqfPI7vw3vxvXIVFEyB6T+iyj/+qIeb8/vhiW8mcPGDH3HbK+t46dvTSEuI7qb8B4V7aLyjoazdpyflVVYREREJiYpiyBgCxoQ7iYhIWBz3ZWXGmP7Ay8DXrbXrgpYnGGOSmueBs4A2RzzrEaIT4KTvwq3L4NzfJ27OpAAAIABJREFUw55SeOoSkp/7Cqx5zRnh4CgMykzkkasnUrK7huufXExtQ1M3BRcRERERkSNqLg6JiPRRnRnK/hlgPjDMGFNijPmmMeYmY8xN7iY/A9KBB1oNWZ8N/NsYswz4GHjNWvtGN/wMoeWLhcnXwy2fwvn3Ymp3w7NXwp9PhRUvQ6DzRZ5JhX7+cOlYFm/ZxQ9eWEYgYLsxuIiIiIiIHKZuH+zbpuKQiPRpnRmt7IoO1n8L+FYbyzcCY489WoSLioYJ17BnwDn4S9+Feb+HF69zRjg49Ycw6mLwdnzV3vlj+7Ftdw2/eX0N+Wlx3HHOiBCEFxERERER4GBn1BqpTET6sK4erazv8UTB2Mvhuwvhkr+Cxwev3AD3TYQlT0JjfYcPccP0gXxtan/+/P5G/rZgSwhCi4iIiIgI4FxSBhqpTET6NBWHuorHC6Mugpv+DZc9BbHJMOs/4E/j4ZPHoamh3V2NMdx5/kjOHJ7Fz/+xgndWl4Uut4iIiIhIX1axDowX/EXhTiIiEjYqDnU1jwdGfBlueB+uehESs+Gft8KfJsCSv7VbJIryevjTlScysl8KNz/9KctLdoc4uIiIiIhIH1RRDGkDICom3ElERMJGxaHuYgwM+RJ862248gWI98Osm+G+SbD0GWhqPGyX+Ogo/nLtRPwJ0Xzj8cVsrToQhuAiIiIiIn1IRbH6GxKRPk/Foe5mDAw9C65/D654FmKS4NWb4P7JsPz5w0Y3y0qK5YlvTKK+sYnrHl/EngPtX44mIiIiIiLHIdAEVRs0UpmI9HkqDoWKMTDsHLhxntMnkS8OXr4eHpgKn70IgUDLpoOzknj46olsqdzPDX9bTF1j0xEeWEREREREjsmerdBYq86oRaTPU3Eo1Ixx+iS68QO49Amn87uXvgkPToOVr7QUiaYOTOf3l45l4aYq/vPF5VhrwxxcRERERKSXaR6pTJeViUgfp+JQuHg8MPIr8O2P4JLHwAbghWvhoVNg9T/BWmaOy+NHZw/j1aXb+MkrK2hsCnT4sCIiIiIi0kkqDomIABAV7gB9nscDoy6GE74CK16G9++G574GOaPh9J/wndNmsL+ukQfmbmDn3lr+dOWJxEfr1yYiIiIictwq1kFcGiSkhzuJiEhYqeVQpPB4Ycyl8J2FcOGfoa4anr0C88gX+PHALdx1wQjeW7uTKx5ZSEV1XbjTioiIiIj0fBqpTEQEUHEo8nijYOzlcPNimPkA1OyCp7/K1z84k48GPcGJZS9y2/3Ps7m8OtxJRUQ6ZIyZYYxZa4xZb4y5vZ1tvmqMWWWMWWmMeTrUGUVEpA+rLFZn1CIi6LKyyOWNghOvgjFfhVX/gA3vkrNpHnd65kDNY5Tf//+oGnQa/pFfhKJTIa0w3IlFRA5hjPEC9wNfAkqARcaYWdbaVUHbDAHuAE621u4yxmSFJ62IiPQ5tXugukzD2IuIoOJQ5PP6YPQlzs1a2LWJ8s/eZum8WYxb/x5seNXZLrU/FE2HwulOsSi5X3hzi4jAZGC9tXYjgDHmWWAmsCpom+uB+621uwCstTtDnlJERPqmivXOVJeViYioONSjGAP+gWSedgPjxl/DNx//mJrtq/nvsbuYzGew+l/w6d+dbdOHOMWiolOh8FRIyAhvdhHpi/KArUH3S4AprbYZCmCM+RDwAndaa99o/UDGmBuAGwDy8vKoqqrqlsDHqqamJqIyRVoeiLxMkZYHIi9TpOWByMsUaXnkKFWsc6ZqOSQiouJQT5WZFMOzN57EzU/H8tVPd/Kd02fyox8PwZStgE0fwKZ5sPw5WPwXZ4fsUSSkj4DMgZCSD8l5kFIAKXkQnRDeHyaUmhph1ybnn4HytWA8UDAZ+p0IvrhwpxPpi6KAIcDpQD4wzxgz2lq7O3gja+3DwMMA48aNs36/P9Q5j6iqqopIyhRpeSDyMkVaHoi8TJGWByIvU6TlkaNUsQ48UeqeQUQEFYd6tPjoKB7++gT+3z9W8MDcDezYU8vdF48hOncsTLsZmhpg21LYPA82zcO35QNY9SJgD32guDSnYJRS4BaN8g+9JeY4fSB1hrXO8zbWQEPQreV+LTTVuUOGZjktmmJTnFZRXal+v/OBX1HsFIEq1jrzlRsg0HD49h4f5I6F/lOdYlHBFEjK6dpMIn1PKVAQdD/fXRasBFhorW0ANhlj1uEUixaFJqKIiPRZlcXgH+h04yAi0sepONTDRXk9/PrC0fRLieMPb61j5746HvzaeJJifc4HXcEk53bqD9hdVYU/JQn2boO9pbCnBPZshT3u/K4tsOVDp3O+YMYLSblOoSje7xZ7aqHhgFPsOaT4UwO26eh+CG80JGQevCVmEedNhPT+7v3mdVkQn36wUGUt7K9wCz/roHzdwSLQnqArWYwX/EWQMQyGneNcV54xzGlC3FQPWz+GrQud28ePwPz7nP1SBwQVi6ZC1gjweI/9lyXS9ywChhhjinCKQpcDV7ba5lXgCuCvxpgMnMvMNoY0pYiI9E0VGqlMRKRZp4pDxpjHgC8DO621o9pYb4D/A84FDgDXWmuXuOuuAX7qbvora+0TXRFcDjLG8B9nDiEnJZY7Xv6MSx+az+PXTSYnJfbwjb0+SBvg3NpTu9ctHpU6RZaWQlIJ7N4KvljnEqyYHGfafItqno8FXzxEudPW971RcKDKKezsL4f9O5356p3O/Z2riK0uh0B9Wz+tU6CKz3D2q9l1cJUv3in49D8JMq85WATyD4So6PZ/3uHnOjeAxnrYvswtFi2ADe85l+cBxCRD/kSnVVHBFGc+JqnD349IX2WtbTTG3AzMwelP6DFr7UpjzC+BxdbaWe66s4wxq4Am4EfW2srwpRYRkT6hqdFpUT50RriTiIhEhM62HHocuA94sp315+BcBjAEp7PRB4Epxhg/8HNgIs61TJ+4wxjvaudx5DhcOrGA7ORYvv33T7jogQ95/BuTGZp9DMWL2GTnljWi60N20q7KSvzxXreA5BaNqncGFZTKYcA0pwCU6RaBkvPA4zm+J46KPtjaipvdEeI2u62LFjjTuXcD1umvKHsk8f4R4M9zLo9ruaW6t6BlRypQifRS1trZwOxWy34WNG+B77s3ERGR0Ni9xelqQJ1Ri4gAnSwOWWvnGWMKj7DJTOBJ95/8BcaYVGNMLk4Ho29Za6sAjDFvATOAZ44ntLRv+tBMnrvxJK57fBGXPPgRD189kakD08Md6+gZA3Gpzi1jcHhz+Iuc29jLnGW1e6BkcUvBKHrTO7Bmb9t9GQXzxbcqHgUVjuJSITHb6ecoKdeZJmbrGngRERGR7lBR7Ew1jL2ICNB1fQ61NVxx3hGWHybShyluTyQOYdovDv565UhufmE1X//LQu46bzBnDc+IyKztifis/hOd29gbqampIS42FhprMXV7MXV78dQ7U1O7x5kG3ffU7cXU78Ps2YbZuQZT525jA4c9TSAunUBCNoHEbAIJWdiELOd+yzQbG5/ujLRxJNZCUx2msZa66t3s3r0J01jrZG6oxTQ1z9eADYA3GuuNhqgYZ+qNxnpjnGlUzMH13piW5W12Km6t+7j7MfX7nWnDAUz9fmhovt+87sAh29FQQ2xUPLVJOQTiMwkkZGLjMw7Ox/ojqg+oiH/PioiIyEGVbnEoPYxfQoqIRJCI6ZA60ocpbk+kDmHq98OrN6dz/ZOLuX1WMdVNUVw0MjUis7YlUl/Xthyatc3aZ8cCAThQCfu2w74dLVPPvu14mu9XrHYusWtdRDIep7PupBynpVHLCHFBnYY31hzXz9gpbrGIqGinWNVQA/XVh+dtj/FCTCJEuzdfHN7dm/Funec8Tlvbux2Yk5jt3rJaTbMhKiao0/TaY5jWOh2XG0/bN48XjIe6+gZiYuPaXNf2Mq8773UuhzxsmbfVPt6IKoaJiIgcDWPMDJw+Sr3Ao9bau1utvxb4HQdHtbzPWvuou67r+zCtWOf0YRnfM/7fFBHpbl1VHGpvuOJSnEvLgpfP7aLnlA6kxkfzt29O4fvPL+VXr61mVUkWd12UTEJMxNQEpZnH44zKlpgJuWPa366p0elvqVURqWVqm5xCUXOn4VGtOwiPY399gISUjMOWO9vHOgWIxnpoqnOmjbWt5uuhsS5ovjZoe/cWaABfglvsSThY8IlOCCoAtVoeFXNY66M9zYW3umqnMFa9E6rLgqZB8ztXOdNA4/H9Lppfs+Cp1wdYp4hng29NLfNRTe7ztrEOG3D3dZcFmg7Oy1H7fFctc1bu4MzhWUR5j7OfMRER6XbGGC9wP/AlnCsJFrn9kK5qtelz1tqbW+3bPX2YVhTrkjIRkSBdVSWYBdxsjHkWp0PqPdba7caYOcCvjTFp7nZnAXd00XNKJ8T6vNx3xXju8a/lz+9vYPHWD/jdJWOY0hP7IRJnpLfkXOd2jOqqqkjoIa2yWsQkOjf/wCNvFwhA7W6nUNZcOGqqO3QkvbaKP83TNgpUnbXnWFq7WRtUPHILRi2FI3v4skAT/KKD16APqGsMcOPfPiEvNY4rp/Tn8kkFpCfGhDuWiIi0bzKw3lq7EcA9Z5gJtC4OteVsuqMP04piGH7ecT2EiEhv0tmh7J/BaQGUYYwpwane+wCstQ/hjERzLrAeZyj769x1VcaYu4BF7kP9svnALqHj8RhuP2c4E/rFctecTVz+yAKunVbIj88eTly0LlORXsTjcZqHx/sh+4Rwp+mYMc7lYnjV+fhRGJIZz91fm8CT8zfzuzlr+b+3i/ny2FyuOamQsQWp4Y4nIiKHa6sf0iltbHexMWY6sA64zVq7tZ19D7uO/2j6LzU1u0g7UMGB+DxqI6C/wJ7Ub6Gydg9l7R49KSuEP29nRyu7ooP1FvhuO+seAx47+mjS1SYUJPPG907lt6+v4a8fbmbu2nJ+f+kYJgzoYa1IRKTPmzEqhxmjcigu28eT87fw8pISXl5SytiCVK6eOoDzxuQS61PxW0SkB/kn8Iy1ts4YcyPwBHBGZ3c+qv5LP3c6o47vP474CGhN3XP72oxsyto9lLX7hDuvOmvoY+Kjo/jFzFE8/a0p1DcGuOSh+fx69mpqG5rCHU1E5KgNyU7irq+MYsFPzuQXF4xkX20DP3hhGdPufpd73lhD6e4QdMYuIiIdaa9/0hbW2kprbZ1791FgQmf3PWoaqUxE5DAqDvVR0wZnMOe26VwxuT8Pz9vIefd+wKefH1+/fiIi4ZIU6+OaaYW88/3T+Ps3pzBxQBoPvb+BU3/7Ljf+bTEfrq/AaeQqIiJhsAgYYowpMsZEA5fj9FnawhgT3KHiBcBqd34OcJYxJs3tx/Qsd9mxq1jnjLKaOuC4HkZEpDfRsFV9WGJMFL++cDQzRubwny8t5+IHP+Km0wZx6xeHEBOlyzFEpOcxxnDKkAxOGZJBya4DPLXwc579+HPmrCxjcFYiV580gIvG55OoURtFRELGWttojLkZp6jjBR6z1q40xvwSWGytnQXcYoy5AGgEqoBr3X27vg/TivXgH+QM9CEiIoCKQwJMH5rJnNum86t/reKBuRt4e3UZf7h0HKPzU8IdTUTkmOWnxfOfM4Zz65lDeG35dp6Yv5mf/WMl97yxlovG53H1SQMYnJUU7pgiIn2CtXY2ziA2wct+FjR/B+2MatzlfZhWrIOs4V32cCIivYEuKxMAkmN93HPJWP567ST21DTwlQc+5I9vrqW+MRDuaCIixyXW5+XiCfnMuvkUXv3uyZx1QjbPfryVL/5xHlc9uoA3VuygsUnHOhGRPqGpAXZtgoyh4U4iIhJRVBySQ3xheBZvfu80Zo7rx73vrmfm/R+yatvecMcSEekS4wpS+eNl4/jojjP40dnD2FS+n5v+/gnT73mP+99bT2V1XccPIiIiPdeuzRBoVHFIRKQVFYfkMCnxPv741XE8cvVEyvfVccF9/+bed4pp0DfrItJLZCTG8N0vDGbej7/An78+gaLMBH43Zy0n/eZdvv/8UpZu3R3uiCIi0h0q1jnT9CHhzSEiEmHU55C060snZDNxQBo/n7WSP761jjdX7eAPl45jWI766BCR3iHK6+HskTmcPTKH4rJ9/G3BFl76pISXl5QyNj+Fq08q5LwxucT61Em/iEiv0FwcytAw9iIiwdRySI4oLSGae684kQevGs/23bWcd+8H/Hr2avbVNoQ7mohIlxqSncQvZ45iwU/O5JczR1Jd18gPXljGtLvf5Z431lC6uybcEUVE5HhVrIfEbIjVwCsiIsHUckg65ZzRuUwu8nPPG2t55IONvPJpKbfPGM6FJ+bh8ZhwxxMR6TJJsT6uPqmQr08dwEcbKnly/mYeen8DD72/gS+OyOaaaYVMG5SOMTr2iYj0OBXr1N+QiEgbVBySTktPjOG3l4zhyin9+fmslfzghWU8tXALv7hglIa9F5FexxjDyYMzOHlwBqW7a3hqwRaeXbSVN1eVMSgzgatPKuSi8XkkxfrCHVVERDrDWqc4NOqicCcREYk4uqxMjtrYglRe/vY0fnfJGD6vOsAF9/+bO15erlF+RKTXykuN48czhvPR7Wfwh0vHkhjr4+ezVjL11+/ws3+soLh8f7gjiohIRw5UQu1udUYtItIGtRySY+LxGC6dWMDZo3K49+1iHv9oM68t3873vzSUr00dQJRXdUcR6X1ifV4unpDPxRPyWbp1N0/O38yzi7by5PwtjMjdzEUn5jFzXD+ykmPDHVVERFpr6Yxal5WJiLSmM3g5LsmxPn765RN443unMrYglTv/uYrz7v03H22oCHc0EZFuNa4glT9+dRwL7jiTH3+xkOgoD/89ezVTf/MOX//LQl75tIQD9Y3hjikiIs0qip1phloOiYi0ppZD0iUGZyXx5Dcm8+aqMu761yqufGQh543O5SfnjSAvNS7c8UREuo0/IZrLx+fynS+OZEN5Na9+Wsorn5Zy23PLiI9ewYyROVw4Po9pgzLwqgN/EZHwqVgHUbGQUhDuJCIiEUfFIekyxhjOHpnDaUMzeXjeRh6Yu5531pTxndMHc8P0gcT6vOGOKCIhZoyZAfwf4AUetdbe3Wr9tcDvgFJ30X3W2kdDGrILDcpM5AdnDeO2Lw5l8ZZdvLykhNc+287Ln5aSnRzDzHF5XHhiHiNyk8MdVUSk76kohvTB4NHFEyIirak4JF0u1uflljOHcNH4PH4zew1/fGsdL3yylZ+edwJnnZCt4Z9F+ghjjBe4H/gSUAIsMsbMstauarXpc9bam0MesBt5PIbJRX4mF/m584KRvLN6J698WsJj/97Ew/M2MjwniYvH56t/IhGRUKoshtyx4U4hIhKROlU2N8bMMMasNcasN8bc3sb6/zHGLHVv64wxu4PWNQWtm9WV4SWy5afFc/9V43n6W1OI83m58W+fcPVjH7N+Z3W4o4lIaEwG1ltrN1pr64FngZlhzhRysT4v543J5dFrJrHwJ2fyiwtGEuPzHtY/0f469U8kItJtGutg12aNVCYi0o4OWw515ptfa+1tQdv/B3Bi0EPUWGvHdV1k6WmmDc5g9i2n8rcFW/jjW+uY8b/zuHJKf245cwgZiTHhjici3ScP2Bp0vwSY0sZ2FxtjpgPrgNustVtbb2CMuQG4ASAvL4+qqqpuiHvsampqOpXJAOcPT+b84clsrqxh9qpyZq+q4LbnlhHn+4wzhvo5b2Qmk/qnHFf/RJ3NE0qRlinS8kDkZYq0PBB5mSItjxxB1UawAY1UJiLSjs5cVtbyzS+AMab5m9/WlwU0uwL4edfEk94iyuvhupOLuGBsP/7n7XU8tfBzXvqkhOunD+Rbpw4kMUZXOIr0Uf8EnrHW1hljbgSeAM5ovZG19mHgYYBx48ZZv98f2pQdqKqq4mgz+f0wfkgePznfsnjLLl75tIR/Ld/Oaysrjrt/omPJ090iLVOk5YHIyxRpeSDyMkVaHjmClpHKBoc3h4hIhOrMGXlnv/nFGDMAKALeDVoca4xZDDQCd1trX21n34j+Vrg9Pekbo0jIaoDvT8/j4lF+7v/gc/737WKe/GgzN5ycz4VjsvB5PRGTtbN6UlboWXmVtccrBYKHhMnnYMfTAFhrK4PuPgrcE4JcESW4f6Kfnz+Sd9fs5OUlpYf0T3TR+DxmjssjW/0TiYgcm4p1zlSXlYmItKmrm2tcDrxorW0KWjbAWltqjBkIvGuM+cxau6H1jpH+rXB7etI3RpGU1e+HRwfn8ennu7j79TXc/dYmnl1Sxo/OHs65o3PcbSIja0ci6XXtjJ6UV1l7vEXAEGNMEU5R6HLgyuANjDG51trt7t0LgNWhjRhZYn1ezh2dy7mjc6msruNfy52Rzn49ew13v76GkwdncOGJeZw9MocEtbgUEem8yvWQnAcxieFOIiISkTrzn2WH3/wGuRz4bvACa22pO91ojJmL0x/RYcUh6ZtO7J/GszdMZe7acn77xhq++/QSxuan8J2T8zhbJ9oiPZq1ttEYczMwB2co+8estSuNMb8EFltrZwG3GGMuwGldWgVcG7bAESY9MYZrphVyzbRCNpRX8+qnpbzyaSnff34Z8dErOHtkDheemMfJgzOOq38iEZE+oWKdM4y9iIi0qTPFoQ6/+QUwxgwH0oD5QcvSgANuXxIZwMn0wUsG5MiMMXxheBbTh2by6qel/OHNtdz43CpO+7Sc/5wxnBP6HX1/GyISGay1s4HZrZb9LGj+DuCOUOfqaQZlJvKDs4Zx2xeHHtI/0SuflpKVFMPMcf248MR8HS9FRNpirdPn0JjLwp1ERCRidVgc6uQ3v+AUjZ611tqg3UcAfzbGBAAPTp9D7XVkLX2c12O4eEI+543J5c/vrOaxhds4708fcOG4PG770lAK/PHhjigiElbt9U/01w8388gHmxiek8SFJ+ZxWmE8anwpIuKq3gl1ezVSmYjIEXSqw4KOvvl179/Zxn4fAaOPI5/0QbE+L1+f3I9rTxvGg3M38NcPN/Gv5dv5+kkDuPkLg0lLiA53RBGRsAvun6hqfz3/Wr6Nl5eU8pvX1/AbYGh2IlOK0pk6MJ0pA/1kJMaEO7KISHg0d0atkcpERNql3iwlYqXE+bj9nOFcM20A//tWMX/9cBPPL9rKTacP4hsnFxEX7Q13RBGRiOBPiObqkwq5+qRCNpZX8/KiTSzfUcNLS0r424ItAAzOSmTqQD9TipxiUVaSRj4TkT6ipTiklkMiIu1RcUgiXm5KHL+9ZAzfPLWIe95Yy+/mrOXJ+Zu5dEIBpwzJYHz/NKKjPOGOKSISEQZmJvKNqXn4/X4amgJ8VrqHhRurWLCxkleWlPL3BZ8DMCgzgSkDnZZFU4v8ZCWrWCQivVTlevDFQ1K/cCcREYlYKg5JjzE0O4lHr5nIos1V/M9b63jw/Q3c99564nxepgz0c8rgDE4enMHwnCSM0cg9IiI+r4fx/dMY3z+Nb58+iMamACu27WXBxkoWbqxk1tJtPL3QKRYNzGguFjmti3JSVCwSkV6ieaQyj75MFBFpj4pD0uNMKvTz9PVT2VvbwIINlXy4voIP1lfwq9dWA5CRGMPJg9M5ZXAGpwzJIDclLsyJRUQiQ5TXw7iCVMYVpHLTaU6xaNV2p1i0YGMV/1q2jWc+dopFRRkJTCnyt/RZpGOpiPRYFcWQPyncKUREIpqKQ9JjJcf6OGtkDmeNzAFg2+4aPlxfwb/XV/Dh+gr+sXQbAAMzEzjVbVU0dVA6ybG+cMYWEYkYUV4PY/JTGZOfyg3TB9EUsKxqblm0qZLXPtvOs4u2AjAgPZ6pbn9FUwem0y9VxSIR6QEaamD35zDuynAnERGJaCoOSa/RLzWOSycWcOnEAqy1rC3bx7+LnWLR84tLeGL+Frwew9j8FLdVUSbjClLVX5FIL9DQ0EBJSQm1tbVhef5AIEBZWVnIni82Npb8/Hx8vq4tdns9htH5KYzOT+H66QNpClhWB7Usen3Fdp5b7BSL+vvjD2lZlJ8W36VZRKR3McbMAP4P8AKPWmvvbme7i4EXgUnW2sXGmEJgNbDW3WSBtfamTj9x5QbAQsaQYw8vItIHqDgkvZIxhuE5yQzPSeZbpw6krrGJTz/f7VyCVlzBfe+t595315MUG8WXRmRzzuhcTh2SQaxPI6CJ9EQlJSUkJSVRWFgYlj7HGhsbiYoKzUeqtZbKykpKSkooKirq1ufyegyj8lIYlZfCt051ikVrduxlwcYqFm6s5M1VZbzwSQkA+WlxTqHILRgV+FUsEhGHMcYL3A98CSgBFhljZllrV7XaLgm4FVjY6iE2WGvHHdOTVxY7U41UJiJyRCoOSZ8QE+V1RuQZmM4PzhrGnpoG5m+o5O3VZby5cgcvf1pKQrSXM0dkc86oHE4flkVctApFIj1FbW1t2ApDoWaMIT09nfLy8pA/t9djGNkvhZH9UvjmKUUEAk4rTadlUSXvrC7jRbdYlJca13IJ2kkD04m3NuR5RSRiTAbWW2s3AhhjngVmAqtabXcX8FvgR132zBVuccg/qMseUkSkN1JxSPqklDgfM0blMGNUDvUXjmb+xkreWLGdOSvLmLVsG3E+L18YnsmMUbmcMTyLxBj9qYhEur5QGGoWKT+rx2MYkZvMiNxkrjvZKRat27mPhRurWLCxkrlry3l5SSkASTFeBmYmUpiRQGF6AkUZzq0wI4GUOPUFJ9LL5QFbg+6XAFOCNzDGjAcKrLWvGWNaF4eKjDGfAnuBn1prP2j9BMaYG4AbAPLy8qiqqgIgYdsKopLy2FNdC4Tn0uOO1NTUtOSNdMraPZS1e/SkrBD+vDrjlT4vOsrDaUMzOW1oJnfNDPDxpipeX7GDN1buYPZnO1rWnzs6hzNHZKtDaxGRdng8By/pvWZaIYGAZX15NQs3VrL88wp2VDexePMuZi3bRnBDIn9CNIXp8RRmJFCU7hSMmgvov8l0AAAelklEQVRHKs6L9H7GGA/wR+DaNlZvB/pbayuNMROAV40xI621e4M3stY+DDwMMG7cOOv3+50Ve7dA1jBa7kegqqqqiM4XTFm7h7J2j56UFcKfV/9xiQSJ8nqYNjiDaYMzuPOCkXyyZRezP9vOGyt28NaqMnxewymDMzhndC5nnZBNanx0uCOLSITYvXs3Tz/9NN/5zneOar9zzz2Xp59+mtTU1G5KFj4ej2FodhJDs5OoGpbc8g9PbUMTW6sOsKliP5sr97Op4gCbK/Yzf0NlS0ujZplJMW7B6NDiUWF6gi7/Fek5SoGCoPv57rJmScAoYK7bMjIHmGWMucBauxioA7DWfmKM2QAMBRZ3+KzWQuV66P+1LvkhRER6MxWHRNrh9RgmF/mZXOTnZ18+gaUlu3n9s+3M/mwH761dzk88hpMGpXNqUTIXTIgnJyU23JFFJIx2797NAw88cFhxqKPOqmfPnt3d0SJOrM/LkOwkhmQnHbaupr6JzZX72Vyxn03udHPFAd5bW0754pJDts1JjqUwI56ijESKMuJbLlcr8MdrgAGRyLIIGGKMKcIpCl0OtIwtb63dA2Q03zfGzAV+6I5WlglUWWubjDEDgSHAxk49677tUF+tkcpERDpBxSGRTvB4DOP7pzG+fxo/OXcEK0r3MnvFdl7/bDu/Lq7g129uZGS/ZM4cnsUXhmcxNj8Vjycy+gQR6Wt+8c+VrNq2t+MNj8IJ/ZL5+fkjj7jN7bffzoYNGxg3bhw+n4/Y2FjS0tJYs2YN69at4ytf+Qpbt26ltraWW2+9lRtuuAGAwsJCFi9eTHV1Neeccw6nnHIKH330EXl5efzjH/8gLi6uS3+WSBcX7W3px6i16rpGp1jkFo02VjjTOSt3ULW/vmU7Y6BfSpx7aZpTNBqY6bQ2KvDH4/N6QvkjifR51tpGY8zNwBycoewfs9auNMb8ElhsrZ11hN2nA780xjQAAeAma23nOuWoWOdM01UcEhHpiIpDIkfJGMPo/BRG56fw47OHsWhdKZ9sr+PdNWXc99567n13PRmJ0Zw+LIszhmdx6pAMktRPkUivd/fdd7NixQqWLl3K3LlzOe+881ixYkXLcPOPPfYYfr+fmpoaJk2axMUXX0x6evohj1FcXMwzzzzDI488wle/+lVeeuklvvY1XQ7RLDEmilF5KYzKSzls3Z6ahpbC0aaK5pZHB5i1dBt7axtbtvN6DPlpcRSkRHPSkGwmF/kZk59CTJRaGol0J2vtbGB2q2U/a2fb04PmXwJeOqYnrdAw9iIinaXikMhxMMYwODOeycPy+fbpg9i1v555xeW8s3onb61yhnT2eZ3L084Yns0Zw7MoykgId2yRXq2jFj6hMnny5JbCEMC9997LK6+8AsDWrVspLi4+rDhUVFTEuHHjAJgwYQKbN28OWd6eLiXOx9iCVMYWHNp3k7WWXQcaWgpGzcWjVaW7+d2ctYAzMMG4/FQmFaUxqdDPhAFpKuqL9AYVxRCdBEk54U4iIhLxVBwS6UJpCdHMHJfHzHF5NDYFWPL5bt5ZU8a7q3dy179Wcde/VjEwI4EzhjutiiYW+omO0uUNIr1RQsLBQvDcuXN5++23mT9/PvHx8Zx++unU1h4+pHJMTEzLvNfrpaamJiRZezNjDP6EaPwJ0UwYkNayvKqqChOTyKLNVSzaXMXHm3fx0Psbuf+9DXgMjMhNdvqdK/QzsdBPZlLMEZ5FRCJSxTrIGOxcayoiIkfUqeKQMWYG8H841wg/aq29u9X6a4HfcXDUgfustY+6664Bfuou/5W19okuyC0S8aK8npYOre84ZwRbqw7w7pqdvLNmJ0/O38Kj/95EUkwU04dm8oXhWZwyOEOdWov0YElJSezbt6/NdXv27CEtLY34+HjWrFnDggULQpxO2pKWEM1ZI3M4a6TTquBAfSOffr6bhZuqWLSpimc+/py/frgZgIEZCUwq9DPJLRgV+OMwOuEUiWwVxVB4crhTiIj0CB0Wh4wxXuB+4EtACbDIGDPLWruq1abPWWtvbrWvH/g5MBGwwCfuvru6JL1ID1Lgj+eaaYVcM62Q/XWNfLi+gnfX7OTdNTt57bPtgHPyMXVQOtMGpTN1YDoZifqmWqSnSE9P5+STT2bUqFHExcWRnZ3dsm7GjBk89NBDjBgxgmHDhjF16tQwJpX2xEdHcfLgDE4e7AyaVN8YYMW2PSza5LQuemPlDp5bvBWA7OQYJhelM7kwjUlFfoZmJWkgApFIUr8f9paoM2oRkU7qTMuhycB6a+1GAGPMs8BMoHVxqC1nA281jyhgjHkLmAE8c2xxRXqHhJiolm+rrbWs2r6X+Rsq+WhDJbOWbuPphZ8DMDQ7kWmDMpg6MJ2pA/2kxkeHObmIHMnTTz/d5vKYmBhef/31Ntc19yuUkZHBihUrWpb/8Ic/7PJ8cnSiozwtI1XeeNogAgHLup37WLTJuQxt0aYq/rlsG+D0eTRxgFMomlToZ3Reii4bFgmnyvXOVMPYi4h0SmeKQ3nA1qD7JcCUNra72BgzHVgH3Gat3drOvnltPYkx5gbgBoC8vDyqqjo3QmW41dTUKGs36GtZc2PhopGpXDQylcbAQNaUVbNoy14Wfb6HZz/+nMc/2owBhmUlMLF/MhP7pzC+IInEmKPvNqyvvbah0pOyisix8XgMw3OSGZ6TzNdPKsRaS8muGj7eVMXHbuuid9bsBCDW5+HEgrSWy9BO7J9KwjEcs0XkGGmkMhGRo9JV/6X8E3jGWltnjLkReAI442gewFr7MPAwwLhx46zf7++iaN2rqqoKZe16fT1rVkY6090Bl+obAywr2e22LKrg+aVl/H3xdrwew6i8FE4a6FyGNrEwjfjojv+k+/pr2116UtZQ6qjPuqDtLgZeBCZZaxeHMKLIMTPGUOCPp8Afz8UT8gEo31fH4s1VfOx2dH3fu8UELM4xu18ykwqdvugmFfpJS1BrUJFuU1EMGPAPDHcSEZEeoTPFoVKgIOh+Pgc7ngbAWlsZdPdR4J6gfU9vte/cow0p0pdFR3mcTlAL/dxy5hBqG5pY8vku5m+oZP6GSh79YCMPvb8Bn9cwLCeJodlJDM9JYlhOMsNzkshKilGnqRIWne2zzhiTBNwKLAx9SpGulZkUwzmjczlndC4A+2ob+GTLLmdUtE27eHKBMyABwJCsxJaWRZOK/MSFM7hIb1OxDtIGgE+DfYiIdEZnikOLgCHGmCKcYs/lwJXBGxhjcq212927FwCr3fk5wK+NMc1jx54F3HHcqUX6sFifl2mDMpg2yOkwdX9dI4u3OMWildv28O/iCl5ecrB+mxrvCyoYJZEbZ5kYn0RyrC9cP4L0HZ3ts+4u4LfAj0IbT6T7JcX6OH1YFqcPywKgtqGJz0r3tFyK9s+gfuZS46IYnJXEoMxEBmUlONPMRPLT4ojyqv8ikaNSWaxLykREjkKHxSFrbaMx5macQo8XeMxau9IY80tgsbV2FnCLMeYCoBGoAq51960yxtyFU2AC+GVz59Qi0jUSYqI4bWgmpw3NbFlWtb+etTv2sa5sH2t27GPtjr28vKSU6rpGd4uV5KXGMTQ7saWF0bCcJAZmJhAT5Q3PDyK9UYd91hljxgMF1trXjDHtFoc66pcuEAjQ2NjY1q4hEY7nDwQC7fZzFYl9YEVapnDmGZQMg8b6uWKsn6aApbj8AEtL9rJ2xz627qnnrVU7eG5xQ8v2Pq+hf1oshf44CtPjKPTHUeSPY4A/joSY7jtmR9rvDCIvU6TlkSAV66FwerhTiIj0GJ3qc8haOxuY3WrZz4Lm76CdFkHW2seAx44jo4gcJX9CNCcNSuekQekty6y1lO6uYXHxdrYdsKzdsY+1O/bx7/UVNDRZAKI8Tv8Z2ckxZCfHkp0cS1bSwfnm5bE+FZDk+BljPMAfcb9QOJKO+qUrKysjKip8nf02NjaG/Pk9Hk+7/VxFYh9YkZYpkvJkZqQzbcShmXYfqGdD+X42lFc7t5372Vhezdz1u2gK2JZ9c5JjD2ll1NzqKCc59rgvKY6k16hZpGWKtDziMIEGaKzRSGUiIkdBw2aI9BHGGPLT4okfnHbIP7INTQE2VexvKRZtqtxP+d46lm7dzY49tdQ1Bg57rOTYqIPFI7dglOMWj7Lc+dyU4z8xkR6voz7rkoBRwFz3vZIDzDLGXNAXOqVOTEykurqabdu2ccstt/Diiy8ets3pp5/O73//eyZOnBiGhBJOqfHRTBgQzYQBaYcsr28M8HnVftbvDCocle/nlSWl7Ks72HotIdrLwMxEBmU6haPBWYkMykpkQHq8WohK79dY50xVHBIR6TQVh0T6OJ/Xw9BspyPr88ceus5ay96aRsr21VK2t5ayvXWU7a1lZ/P8vloWbtxP2d5aGoO+yQbnxGRwdhJDsxIZmp3E4Gxn2k9Fo77kiH3WWWv3ABnN940xc4Ef9oXCULB+/fq1WRgSaUt0lIfBWUkMzko6ZLm1lvJ9dax3i0UbdjqFo0Wbd/Hq0m0t23kM9PfHuy2MDhaPBmUmavQ06TVMU3NxSH0OiYh0lopDItIuYwwp8T5S3E6t2xMIWKoO1LuFozpKd9ewfmc168r28d7acl74pKRl28SYKAZnJTLULRYNyU5iaHZil1wCIZGlk33Wdb3Xb4cdn3XtY+aMhnPuPuImt99+OwUFBXz3u98F4M477yQqKor33nuPXbt20dDQwK9+9Stmzpx5yH6bN2/my1/+MitWrKCmpobrrruOZcuWMXz4cGpqarr255BeyxhDVnIsWcmxLQMWNDtQ38jGlkvU3OnOaj5YX0F9UOtQf0L0IcWi9JgARbmGjMQYMhJjiItWiyPpGUxjHcSmQEJmxxuLiAig4pCIdAGP5+DJw8h+h6/ftb+edWX7WLezmuIyp6Psd1bv5PnFB4tGSbFRDMk6tGCU5KknPqlJfRz1YB31Wddq+emhyNRdLrvsMr73ve+1FIeef/555syZwy233EJycjIVFRVMnTqVCy64oN1C6IMPPkh8fDyrV69m+fLljB8/PpQ/gvRS8dFRjMpLYVReyiHLmwKWbbtrnNZGOw8Wjt5eXcazi7a28The91gfTbp7zM9IjCYjMYZ0d9q8LCXOp4K/hE9THaSPBr0HRUQ6TcUhEel2aQnRTBmYzpSB6Ycsr6yuY11ZNcU7nYLRurJq5qzccdhJSVq8j5yUOHJTYslJiSU32Zn2S40jJ8Xp4yghRoczcXXQwqe7nHjiiezcuZNt27ZRXl5OWloaOTk53HbbbcybNw+Px0NpaSllZWXk5OS0+Rjz5s3jlltuAWDMmDGMGTMmlD+C9DFedxCCAn88XxiWdci63QfqWbFpBw3eWMqr66iorqOyur5lurXqAJ9+vpuq/XW0uqoYcAY4SG8pHDkFo8ygIlJ6UGHJnxCNz+sJ0U8tfYFprNMlZSIiR0lnUyISNumJMZyUGHPYqGoV1fUU79xHcWkF+xq9bN9Ty449tWzfU8vSrbup2l9/2GMlx0aRm+IUi1qKSCmxZCTGkBznIyk2iuRYH8lxPhKivfpGW7rFpZdeyosvvsiOHTu47LLLeOqppygvL+eTTz7B5/NRWFhIbW1tuGOKdCg1PpoTchM7HImrKWDZdaC+pXDk3JqLSM58ZXUdG3ZWU15dd8hlbMHS4n0tBaP0xBinkJQQTUZSzMGWSQkx1OyvJxBdh8cYvMbg8eDMewweY/AYp+ilY3wfF2hQZ9QiIkdJxSERiSjGGDKTYshMimF4WttDddc2NFG2t/aQotH2PTUt91du20tFdV27z+H1mKBikTt155OC5oOLSR73xMMYp0NXYw6eiHiMYd/e/aTURrWcqARvY3BPXrwGn8c5iYnyeohy531eT8v20rNddtllXH/99VRUVPD+++/z/PPPk5WVhc/n47333mPLli1H3H/69Ok8/fTTnHHGGaxYsYLly5eHKLnIsfEGXVY8jPb7pgOn+F9d19hSMKqorqM8aL65wLR6217mVdexr7bxiI93JMY9Nh9SQDLGPZYTVExyC0ttbHNI0clj8LrLnHlnn0BTIwmxMfi8HqKjPPi8zi3aaw69H+Uh2uvB5zX4opq3Cd7HtLrvITrKEO314osyQY/rbOP16POiQyoOiYgcFRWHRKTHifV5GZCewID0hHa3qW8MULa3lqr99eytbWBfbSN7axrYW9vA3ppGd+our21gU8X+lmX765tC+NMcFOUxRHkNUR6PWzRyC0keD1HuvES2kSNHsm/fPvLy8sjNzeWqq67i/PPPZ/To0UycOJHhw4cfcf9vf/vbXHfddYwYMYIRI0YwYcKEECUX6X7GGJJifSTF+ijKaP/43ay2oYmq/fWHFI527dlHbHw8gYClyToDIgSspclad95pzWTdZU0BpyjVFHDuW3e9M+8ub97Gvd/mNjbocdxbfZOlrr6JPbW1NDQF3JulrjEQdN9Z1tU8hsMLTG4RSVy6rExE5KioOCQivVJ0lKelL42j1dgUoLqusaWItL+ukYB7YhCwELDOyYi1YLEEArB33z7iExLb3CZgm09YAjQGLI1NlsaApSngnDQ0Bay7PHDIfKN7AuJsE6AhYHmvG14r6VqffXZwpLSMjAzmz5/f5nbV1dUAFBYWsmLFCgDi4uJ49tlnuz+kSA8Q6/PSLzWOfqlxLcuqqqo6vNQtlDqTJxCwNLjH+4bGAPVNAeobDxaO6t1lzcWk5nX1QdsfXG6D9m37sfQ5ATYmGdKKwh1DRKRHUXFIRKSVKK+H1PhoUuOjO71PVZUvJCcsD1zV7U8hIiJdyOMxxHi8xEQBMd3/fA98rfufI9I1pQyAqM5/houICKjtqYiIiIiIiIhIH6bikIiI9ArWdn2/HpGqL/2sIiIiItL9VBwSEZEeLzY2lsrKyj5RNLHWUllZSWxsbLijiIiIiEgvoT6HRESkx8vPz6ekpITy8vKwPH8gEMDjCd33LbGxseTn54fs+URERESkd1NxSEREejyfz0dRUfhGpom0EZRERERERI6GLisTEREREREREenDVBwSEREREREREenDVBwSEREREREREenDTCSO7GKM2QesDXeOTsoAKsIdopOUtXv0pKzQs/Iq6+EGWGszQ/A8EStCPyMi7b0aaXkg8jJFWh6IvEyRlgciL1Ok5QEYZq1NCneIcIrQz4kjicT3UXuUtXsoa/foSVkhzOcTkdoh9Vpr7cRwh+gMY8xiZe16ytp9elJeZZV2RNxnRKT9/iMtD0RepkjLA5GXKdLyQORlirQ84GQKd4YIEHGfE0cSie+j9ihr91DW7tGTskL48+qyMhERERERERGRPkzFIRERERERERGRPixSi0MPhzvAUVDW7qGs3acn5VVWaUskvtaRlinS8kDkZYq0PBB5mSItD0RepkjLA5GZKdR62mvQk/Iqa/dQ1u7Rk7JCmPNGZIfUIiIiIiIiIiISGpHackhEREREREREREJAxSERERERERERkT4sbMUhY8wMY8xaY8x6Y8ztbayPMcY8565faIwpDH3KliwFxpj3jDGrjDErjTG3trHN6caYPcaYpe7tZ+HI6mbZbIz5zM1x2HCmxnGv+9ouN8aMD1POYUGv11JjzF5jzPdabRO219UY85gxZqcxZkXQMr8x5i1jTLE7TWtn32vcbYqNMdeEKevvjDFr3N/xK8aY1Hb2PeL7JYR57zTGlAb9rs9tZ98jHjtClPW5oJybjTFL29k35K9tb3M8f4chznSp+/kQMMaEdAjS4/n7D3Gmu9w8S40xbxpj+oUzT9C6HxhjrDEmI1R52svU2eNgqPK4y//DfS+tNMbcE6o87WXq7PE3hHnGGWMWNB/njTGTw5xnrDFmvvvZ809jTHKo8oRDR/8TmAg5nzA97FzCzaPzia7Jp/OJ0GWNyHOJI+SNvPMJa23Ib4AX2AAMBKKBZcAJrbb5DvCQO3858Fw4srrPnwuMd+eTgHVt5D0d+Fe4MrbKshnIOML6c4HXAQNMBRZGQGYvsAMYECmvKzAdGA+sCFp2D3C7O3878Ns29vMDG91pmjufFoasZwFR7vxv28ramfdLCPPeCfywE++TIx47QpG11fo/AD+LlNe2t92O9e8wDJlGAMOAucDECMjTqb//EGdKDpq/pfkzPlx53OUFwBxgS085DoY4zxeAt4EY935WuDO1Wt/u8TeEr9GbwDnu/LnA3DDnWQSc5s5/A7grHO+nEP38PeZ8gh52LuHmOeL/MOh8orOZdD4RuqwdfoZ25rgRqryt1kfE+US4Wg5NBtZbazdaa+uBZ4GZrbaZCTzhzr8InGmMMSHM2MJau91au8Sd3wesBvLCkaWLzASetI4FQKoxJjfMmc4ENlhrt4Q5Rwtr7TygqtXi4PflE8BX2tj1bOAta22VtXYX8BYwo9uC0nZWa+2b1tpG9+4CIL87MxyNdl7bzujMsaNLHSmre0z6KvBMd2boy47j7zCkmay1q621a0OZo4M8Yf37byfT3qC7CUDIRsQ4wt/x/wA/DmWWZsdxHOwW7eT5NnC3tbbO3WZnBGQCwnP8bSePBZpb56QA28KcZygwz51/C7g4VHnCoMecT/TCcwnQ+USn6Hyie/SkcwmIvM+z9oSrOJQHbA26X8LhB8iWbdw35B4gPSTpjsBtjnoisLCN1ScZY5YZY143xowMabBDWeBNY8wnxpgb2ljfmdc/1C6n/T+ISHldAbKttdvd+R1AdhvbROLr+w2cb3fa0tH7JZRudputPtZOE9tIe21PBcqstcXtrI+k17Y36czfoRx0pL//kDLG/LcxZitwFRDuSyZmAqXW2mXhzNGGjo6DoTQUONW9HOd9Y8ykMOcJ1tHxN1S+B/zOfV//HrgjzHlWcvBE51Kc1nG9VY88n+gh5xKg84nupPOJ7tPTziUggs4n1CH1UTDGJAIvAd9r9Q0owBKcJoxjgT8Br4Y6X5BTrLXjgXOA7xpjpocxS4eMMdHABcALbayOpNf1ENZp5xfyb5uPljHmv4BG4Kl2NomU98uDwCBgHLAdp3llpLuCI1f5I+W17bV6yt9huHTi7z+krLX/Za0twMlzc7hyGGPigZ8Q5gJVGyLtOBiFc0nDVOBHwPPhasXdho6Ov6HybeA29319G/CXMOf5BvAdY8wnOJcv1Yc5jwTpQecS0MP+h9H5RPfqIecTkfYZ2lkRcz4RruJQKYd+k5HvLmtzG2NMFE5T3cqQpGuDMcaHczB/ylr7cuv11tq91tpqd3424DMh7twyKEupO90JvILTfC5YZ17/UDoHWGKtLWu9IpJeV1dZc5NZd9pWE/uIeX2NMdcCXwaucj98DtOJ90tIWGvLrLVN1toA8Eg7OSLptY0CLgKea2+bSHlte6HO/B32eZ35+w+jpwjv5S6DgCJgmTFmM86xZIkxJieMmTp7HAylEuBl97KRj4EAEM7PYKBzx98QugZo/r/wBcL8O7PWrrHWnmWtnYBzsrEhnHm6WY86n+hJ5xJuBp1PdB+dT3SDnnYuAZF3PhGu4tAiYIgxpsit8l4OzGq1zSycD1yAS4B3w/XPrfst2V+A1dbaP7azTU7zt2nGGanCQxg+fIwxCcaYpOZ5nE7EWo/OMgu42jimAnuCmjaGQ7vV0kh5XYMEvy+vAf7RxjZzgLOMMWluc8az3GUhZYyZgdOXxgXW2gPtbNOZ90tItLpO/cJ2cnTm2BEqXwTWWGtL2loZSa9tL9SZv8M+rTN//6FmjBkSdHcmsCZcWay1n1lrs6y1hdbaQpwiyHhr7Y5wZYJOHwdD6VWcTqkxxgzF6byzIqyJHEc8/obYNuA0d/4MIKyXuRljstypB/gp8FA483SzHnM+0ZPOJdzn1/lE99L5RDfogecSEGnnEzZEPYq3vuH0cL8O5xuN/3KX/RLnjQcQi/MNzHrgY2BgGLOegtPcbzmw1L2dC9wE3ORuczPOdd7LcDrrmhamrAPdDMvcPM2vbXBWA9zvvvafEeKRdVrlTcA5OKcELYuI1xXnA2Y70IBz4vBNnOvU38H55+9twO9uOxF4NGjfb7jv3fXAdWHKuh7nmtrm92zzaB39gNlHer+EKe/f3PfjcpyDdG7rvO79w44doc7qLn+8+X0atG3YX9vedjuav8MwZ7rQna8DyoA5Yc7T5t9/mDO9hPMPzXLgn0BeOPO0Wr+Z0I9W1unjYBjzRAN/d39vS4Azwv0aucsPO/6G8TU6BfjEPdYvBCaEOc+t7mfkOuBuwIT6dQrx76RHnE/Qg84l3Cw6n+i6bDqfCF3WiDyXaC+vu/xxIuh8wrhPKiIiIiIiIiIifZA6pBYRERERERER6cNUHBIRERERERER6cNUHBIRERERERER6cNUHBIRERERERER6cNUHBIRERERERER6cNUHBIRERERERER6cNUHBIRERERERER6cP+Px/5MTGbX19ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MiniRocket with data augmentation\n",
    "tfms = [None, TSClassification()]\n",
    "batch_tfms = [TSStandardize(by_sample=True), TSMagScale(), TSWindowWarp()]\n",
    "dls = get_ts_dls(X, y, splits=splits, tfms=tfms, batch_tfms=batch_tfms)\n",
    "model = build_ts_model(MiniRocket, dls=dls)\n",
    "learn = Learner(dls, model, metrics=accuracy, cbs=[ShowGraph()])\n",
    "learn.fit_one_cycle(20, 3e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we can see that using MiniRocket (Pytorch implementation) with data augmentation achieves an accuracy of 69%+, compared to the sklearn-API implementation which is around 65%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have trained the model, you can always save if for future use. We just need to export the learner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('./models/MiniRocket_aug.pkl')\n",
    "PATH.parent.mkdir(parents=True, exist_ok=True)\n",
    "learn.export(PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first recreate the learner: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH = Path('./models/MiniRocket_aug.pkl')\n",
    "learn = load_learner(PATH, cpu=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to generate predictions. We'll confirm it works well with the valid dataset: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array(['6', '62', '6', ..., '42', '90', '42'], dtype='<U2')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probas, _, preds = learn.get_X_preds(X[splits[1]])\n",
    "preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the validation loss & metrics are the same we had when we saved it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6889699918896999"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(y[splits[1]], preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MiniRocket is a new type of algorithm that is significantly faster than any other method of comparable accuracy (including Rocket), and significantly more accurate than any other method of even roughly-similar computational expense. \n",
    "\n",
    "`tsai` supports the 2 variations of MiniRocket introduced in this notebook. A cpu version (that can be used with relatively small datasets, with <10k samples) and a gpu (Pytorch) version that can be used with datasets of any size. The Pytorch version can be used in an offline mode (pre-calculating all features before fitting the model) or in an online mode (calculating features on the fly). \n",
    "\n",
    "We believe MiniRocket is a great new tool, and encourange you to try it in your next Time Series Classification or Regression task. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
